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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.13315">arXiv:2401.13315</a> <span> [<a href="https://arxiv.org/pdf/2401.13315">pdf</a>, <a href="https://arxiv.org/format/2401.13315">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1117/12.2653048">10.1117/12.2653048 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Haugland%2C+M+R">Mathias Ramm Haugland</a>, <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.13315v1-abstract-short" style="display: inline;"> To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep learning based methods for automatic polyp detection have been developed for conventional white-light imaging (WLI). Compared with WLI, narrow-band im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13315v1-abstract-full').style.display = 'inline'; document.getElementById('2401.13315v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.13315v1-abstract-full" style="display: none;"> To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep learning based methods for automatic polyp detection have been developed for conventional white-light imaging (WLI). Compared with WLI, narrow-band imaging (NBI) can improve polyp classification during colonoscopy but requires special equipment. We propose a CycleGAN-based framework to convert images captured with regular WLI to synthetic NBI (SNBI) as a pre-processing method for improving object detection on WLI when NBI is unavailable. This paper first shows that better results for polyp detection can be achieved on NBI compared to a relatively similar dataset of WLI. Secondly, experimental results demonstrate that our proposed modality translation can achieve improved polyp detection on SNBI images generated from WLI compared to the original WLI. This is because our WLI-to-SNBI translation model can enhance the observation of polyp surface patterns in the generated SNBI images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13315v1-abstract-full').style.display = 'none'; document.getElementById('2401.13315v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03756">arXiv:2310.03756</a> <span> [<a href="https://arxiv.org/pdf/2310.03756">pdf</a>, <a href="https://arxiv.org/format/2310.03756">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Nesaragi%2C+N">Naimahmed Nesaragi</a>, <a href="/search/cs?searchtype=author&query=Halvorsen%2C+P+S">Per Steiner Halvorsen</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.03756v1-abstract-short" style="display: inline;"> This work investigates the predictive potential of bipolar electroencephalogram (EEG) recordings towards efficient prediction of poor neurological outcomes. A retrospective design using a hybrid deep learning approach is utilized to optimize an objective function aiming for high specificity, i.e., true positive rate (TPR) with reduced false positives (< 0.05). A multi-channel EEG array of 18 bipol… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03756v1-abstract-full').style.display = 'inline'; document.getElementById('2310.03756v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03756v1-abstract-full" style="display: none;"> This work investigates the predictive potential of bipolar electroencephalogram (EEG) recordings towards efficient prediction of poor neurological outcomes. A retrospective design using a hybrid deep learning approach is utilized to optimize an objective function aiming for high specificity, i.e., true positive rate (TPR) with reduced false positives (< 0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly selected 5-minute segment in an hour is kept. In order to determine the outcome prediction, a combination of a feature encoder with 1-D convolutional layers, learnable position encoding, a context network with attention mechanisms, and finally, a regressor and classifier blocks are used. The feature encoder extricates local temporal and spatial features, while the following position encoding and attention mechanisms attempt to capture global temporal dependencies. Results: The proposed framework by our team, OUS IVS, when validated on the challenge hidden validation data, exhibited a score of 0.57. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03756v1-abstract-full').style.display = 'none'; document.getElementById('2310.03756v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 3 figures, 50th Computing in Cardiology conference in Atlanta, Georgia, USA on 1st - 4th October 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.03575">arXiv:2307.03575</a> <span> [<a href="https://arxiv.org/pdf/2307.03575">pdf</a>, <a href="https://arxiv.org/ps/2307.03575">ps</a>, <a href="https://arxiv.org/format/2307.03575">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mahootiha%2C+M">Maryamalsadat Mahootiha</a>, <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Bergsland%2C+J">Jacob Bergsland</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.03575v1-abstract-short" style="display: inline;"> Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patien… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.03575v1-abstract-full').style.display = 'inline'; document.getElementById('2307.03575v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.03575v1-abstract-full" style="display: none;"> Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.03575v1-abstract-full').style.display = 'none'; document.getElementById('2307.03575v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.06064">arXiv:2303.06064</a> <span> [<a href="https://arxiv.org/pdf/2303.06064">pdf</a>, <a href="https://arxiv.org/format/2303.06064">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Non-invasive Waveform Analysis for Emergency Triage via Simulated Hemorrhage: An Experimental Study using Novel Dynamic Lower Body Negative Pressure Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nesaragi%2C+N">Naimahmed Nesaragi</a>, <a href="/search/cs?searchtype=author&query=H%C3%B8iseth%2C+L+%C3%98">Lars 脴ivind H酶iseth</a>, <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Rosseland%2C+L+A">Leiv Arne Rosseland</a>, <a href="/search/cs?searchtype=author&query=Halvorsen%2C+P+S">Per Steinar Halvorsen</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.06064v1-abstract-short" style="display: inline;"> The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LB… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06064v1-abstract-full').style.display = 'inline'; document.getElementById('2303.06064v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.06064v1-abstract-full" style="display: none;"> The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings, intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time-frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, $F1$-score:72.16%, Sensitivity:79.06 %, and Specificity:89.21 %. Our proposed DL algorithm on PPG signal demonstrates the possibility of capturing the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06064v1-abstract-full').style.display = 'none'; document.getElementById('2303.06064v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.05871">arXiv:2303.05871</a> <span> [<a href="https://arxiv.org/pdf/2303.05871">pdf</a>, <a href="https://arxiv.org/format/2303.05871">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/BIBM55620.2022.9995323">10.1109/BIBM55620.2022.9995323 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Accurate Real-time Polyp Detection in Videos from Concatenation of Latent Features Extracted from Consecutive Frames </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Younghak Shin</a>, <a href="/search/cs?searchtype=author&query=Bergsland%2C+J">Jacob Bergsland</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.05871v1-abstract-short" style="display: inline;"> An efficient deep learning model that can be implemented in real-time for polyp detection is crucial to reducing polyp miss-rate during screening procedures. Convolutional neural networks (CNNs) are vulnerable to small changes in the input image. A CNN-based model may miss the same polyp appearing in a series of consecutive frames and produce unsubtle detection output due to changes in camera pose… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05871v1-abstract-full').style.display = 'inline'; document.getElementById('2303.05871v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.05871v1-abstract-full" style="display: none;"> An efficient deep learning model that can be implemented in real-time for polyp detection is crucial to reducing polyp miss-rate during screening procedures. Convolutional neural networks (CNNs) are vulnerable to small changes in the input image. A CNN-based model may miss the same polyp appearing in a series of consecutive frames and produce unsubtle detection output due to changes in camera pose, lighting condition, light reflection, etc. In this study, we attempt to tackle this problem by integrating temporal information among neighboring frames. We propose an efficient feature concatenation method for a CNN-based encoder-decoder model without adding complexity to the model. The proposed method incorporates extracted feature maps of previous frames to detect polyps in the current frame. The experimental results demonstrate that the proposed method of feature concatenation improves the overall performance of automatic polyp detection in videos. The following results are obtained on a public video dataset: sensitivity 90.94\%, precision 90.53\%, and specificity 92.46% <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05871v1-abstract-full').style.display = 'none'; document.getElementById('2303.05871v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2461-2466). IEEE </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.09835">arXiv:2302.09835</a> <span> [<a href="https://arxiv.org/pdf/2302.09835">pdf</a>, <a href="https://arxiv.org/ps/2302.09835">ps</a>, <a href="https://arxiv.org/format/2302.09835">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.bspc.2022.103491">10.1016/j.bspc.2022.103491 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Younghak Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.09835v1-abstract-short" style="display: inline;"> Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a negative image (image without a polyp) using a sim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.09835v1-abstract-full').style.display = 'inline'; document.getElementById('2302.09835v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.09835v1-abstract-full" style="display: none;"> Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a negative image (image without a polyp) using a simple conditional GAN architecture and then converts the negative image into a new-looking polyp image using the same network. In addition, by using the controllable polyp masks, polyps with various characteristics can be generated from one input condition. The generated polyp images can be used directly as training images for polyp detection and segmentation without additional labeling. To quantitatively assess the quality of generated synthetic polyps, we use public polyp image and video datasets combined with the generated synthetic images to examine the performance improvement of several detection and segmentation models. Experimental results show that we obtain performance gains when the generated polyp images are added to the training set. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.09835v1-abstract-full').style.display = 'none'; document.getElementById('2302.09835v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1907.09180">arXiv:1907.09180</a> <span> [<a href="https://arxiv.org/pdf/1907.09180">pdf</a>, <a href="https://arxiv.org/format/1907.09180">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ISMICT.2019.8743694">10.1109/ISMICT.2019.8743694 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Younghak Shin</a>, <a href="/search/cs?searchtype=author&query=Solhusvik%2C+J">Johannes Solhusvik</a>, <a href="/search/cs?searchtype=author&query=Bergsland%2C+J">Jacob Bergsland</a>, <a href="/search/cs?searchtype=author&query=Aabakken%2C+L">Lars Aabakken</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1907.09180v1-abstract-short" style="display: inline;"> Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.09180v1-abstract-full').style.display = 'inline'; document.getElementById('1907.09180v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.09180v1-abstract-full" style="display: none;"> Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extractor by adding extra polyp images to the training dataset to answer whether we need deeper and more complex CNNs or better dataset for training in automatic polyp detection and segmentation. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance on the 2015 MICCAI polyp detection dataset. The best results achieved are 72.59% recall, 80% precision, 70.42% dice, and 61.24% Jaccard. The model achieved state-of-the-art segmentation performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.09180v1-abstract-full').style.display = 'none'; document.getElementById('1907.09180v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.11467">arXiv:1906.11467</a> <span> [<a href="https://arxiv.org/pdf/1906.11467">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ACCESS.2018.2872717">10.1109/ACCESS.2018.2872717 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Younghak Shin</a>, <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.11467v1-abstract-short" style="display: inline;"> One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.11467v1-abstract-full').style.display = 'inline'; document.getElementById('1906.11467v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.11467v1-abstract-full" style="display: none;"> One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in a generative adversarial networks approach. We propose an edge filtering-based combined input conditioned image to train our proposed networks. This enables realistic polyp image generations while maintaining the original structures of the colonoscopy image frames. More importantly, our proposed framework generates synthetic polyp images from normal colonoscopy images which have the advantage of being relatively easy to obtain. The network architecture is based on the use of multiple dilated convolutions in each encoding part of our generator network to consider large receptive fields and avoid many contractions of a feature map size. An image resizing with convolution for upsampling in the decoding layers is considered to prevent artifacts on generated images. We show that the generated polyp images are not only qualitatively realistic but also help to improve polyp detection performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.11467v1-abstract-full').style.display = 'none'; document.getElementById('1906.11467v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Access 6 (2018): 56007-56017 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.11463">arXiv:1906.11463</a> <span> [<a href="https://arxiv.org/pdf/1906.11463">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ACCESS.2018.2856402">10.1109/ACCESS.2018.2856402 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Younghak Shin</a>, <a href="/search/cs?searchtype=author&query=Qadir%2C+H+A">Hemin Ali Qadir</a>, <a href="/search/cs?searchtype=author&query=Aabakken%2C+L">Lars Aabakken</a>, <a href="/search/cs?searchtype=author&query=Bergsland%2C+J">Jacob Bergsland</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.11463v1-abstract-short" style="display: inline;"> Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.11463v1-abstract-full').style.display = 'inline'; document.getElementById('1906.11463v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.11463v1-abstract-full" style="display: none;"> Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods such as, automatic false positive learning and off-line learning, both of which can be incorporated with the region based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance compared to other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.11463v1-abstract-full').style.display = 'none'; document.getElementById('1906.11463v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Access 6 (2018): 40950-40962 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1401.2568">arXiv:1401.2568</a> <span> [<a href="https://arxiv.org/pdf/1401.2568">pdf</a>, <a href="https://arxiv.org/ps/1401.2568">ps</a>, <a href="https://arxiv.org/format/1401.2568">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Zero-Delay Joint Source-Channel Coding for a Multivariate Gaussian on a Gaussian MAC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Floor%2C+P+A">P氓l Anders Floor</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+A+N">Anna N. Kim</a>, <a href="/search/cs?searchtype=author&query=Ramstad%2C+T+A">Tor A. Ramstad</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a>, <a href="/search/cs?searchtype=author&query=Wernersson%2C+N">Niklas Wernersson</a>, <a href="/search/cs?searchtype=author&query=Skoglund%2C+M">Mikael Skoglund</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1401.2568v1-abstract-short" style="display: inline;"> In this paper, communication of a Multivariate Gaussian over a Gaussian Multiple Access Channel is studied. Distributed zero-delay joint source-channel coding (JSCC) solutions to the problem are given. Both nonlinear and linear approaches are discussed. The performance upper bound (signal-to-distortion ratio) for arbitrary code length is also derived and Zero-delay cooperative JSCC is briefly addr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1401.2568v1-abstract-full').style.display = 'inline'; document.getElementById('1401.2568v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1401.2568v1-abstract-full" style="display: none;"> In this paper, communication of a Multivariate Gaussian over a Gaussian Multiple Access Channel is studied. Distributed zero-delay joint source-channel coding (JSCC) solutions to the problem are given. Both nonlinear and linear approaches are discussed. The performance upper bound (signal-to-distortion ratio) for arbitrary code length is also derived and Zero-delay cooperative JSCC is briefly addressed in order to provide an approximate bound on the performance of zero-delay schemes. The main contribution is a nonlinear hybrid discrete-analog JSSC scheme based on distributed quantization and a linear continuous mapping named Distributed Quantizer Linear Coder (DQLC). The DQLC has promising performance which improves with increasing correlation, and is robust against variations in noise level. The DQLC exhibits a constant gap to the performance upper bound as the signal-to-noise ratio (SNR) becomes large for any number of sources and values of correlation. Therefore it outperforms a linear solution (uncoded transmission) in any case when the SNR gets sufficiently large. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1401.2568v1-abstract-full').style.display = 'none'; document.getElementById('1401.2568v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 January, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 page draft</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1101.5716">arXiv:1101.5716</a> <span> [<a href="https://arxiv.org/pdf/1101.5716">pdf</a>, <a href="https://arxiv.org/ps/1101.5716">ps</a>, <a href="https://arxiv.org/format/1101.5716">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Zero-Delay Joint Source-Channel Coding for a Bivariate Gaussian on a Gaussian MAC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Floor%2C+P+A">Paal Anders Floor</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+A+N">Anna N. Kim</a>, <a href="/search/cs?searchtype=author&query=Wernersson%2C+N">Niklas Wernersson</a>, <a href="/search/cs?searchtype=author&query=Ramstad%2C+T+A">Tor A. Ramstad</a>, <a href="/search/cs?searchtype=author&query=Skoglund%2C+M">Mikael Skoglund</a>, <a href="/search/cs?searchtype=author&query=Balasingham%2C+I">Ilangko Balasingham</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1101.5716v2-abstract-short" style="display: inline;"> In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1101.5716v2-abstract-full').style.display = 'inline'; document.getElementById('1101.5716v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1101.5716v2-abstract-full" style="display: none;"> In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improve with increasing correlation and are robust against variations in noise level. Both schemes exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1101.5716v2-abstract-full').style.display = 'none'; document.getElementById('1101.5716v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 April, 2012; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 January, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2011. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 page draft</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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