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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/2306.06503">arXiv:2306.06503</a> <span> [<a href="https://arxiv.org/pdf/2306.06503">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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="Cryptography and Security">cs.CR</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/10.1148/ryai.230212">10.1148/ryai.230212 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arasteh%2C+S+T">Soroosh Tayebi Arasteh</a>, <a href="/search/cs?searchtype=author&query=Lotfinia%2C+M">Mahshad Lotfinia</a>, <a href="/search/cs?searchtype=author&query=Nolte%2C+T">Teresa Nolte</a>, <a href="/search/cs?searchtype=author&query=Saehn%2C+M">Marwin Saehn</a>, <a href="/search/cs?searchtype=author&query=Isfort%2C+P">Peter Isfort</a>, <a href="/search/cs?searchtype=author&query=Kuhl%2C+C">Christiane Kuhl</a>, <a href="/search/cs?searchtype=author&query=Nebelung%2C+S">Sven Nebelung</a>, <a href="/search/cs?searchtype=author&query=Kaissis%2C+G">Georgios Kaissis</a>, <a href="/search/cs?searchtype=author&query=Truhn%2C+D">Daniel Truhn</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="2306.06503v2-abstract-short" style="display: inline;"> Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data. The use of AI models solely trained on large multi-institutional datasets can help with this, yet the imperative to ensure data privacy remains, particularly as membership inference risks breaching patient confidentiality. As a proposed remedy, we advocate for the integ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06503v2-abstract-full').style.display = 'inline'; document.getElementById('2306.06503v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.06503v2-abstract-full" style="display: none;"> Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data. The use of AI models solely trained on large multi-institutional datasets can help with this, yet the imperative to ensure data privacy remains, particularly as membership inference risks breaching patient confidentiality. As a proposed remedy, we advocate for the integration of differential privacy (DP). We specifically investigate the performance of models trained with DP as compared to models trained without DP on data from institutions that the model had not seen during its training (i.e., external validation) - the situation that is reflective of the clinical use of AI models. By leveraging more than 590,000 chest radiographs from five institutions, we evaluated the efficacy of DP-enhanced domain transfer (DP-DT) in diagnosing cardiomegaly, pleural effusion, pneumonia, atelectasis, and in identifying healthy subjects. We juxtaposed DP-DT with non-DP-DT and examined diagnostic accuracy and demographic fairness using the area under the receiver operating characteristic curve (AUC) as the main metric, as well as accuracy, sensitivity, and specificity. Our results show that DP-DT, even with exceptionally high privacy levels (epsilon around 1), performs comparably to non-DP-DT (P>0.119 across all domains). Furthermore, DP-DT led to marginal AUC differences - less than 1% - for nearly all subgroups, relative to non-DP-DT. Despite consistent evidence suggesting that DP models induce significant performance degradation for on-domain applications, we show that off-domain performance is almost not affected. Therefore, we ardently advocate for the adoption of DP in training diagnostic medical AI models, given its minimal impact on performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06503v2-abstract-full').style.display = 'none'; document.getElementById('2306.06503v2-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">Published in Radiology: Artificial Intelligence. RSNA</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Radiology: Artificial Intelligence, 2024, 6(1), e230212 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.08972">arXiv:2304.08972</a> <span> [<a href="https://arxiv.org/pdf/2304.08972">pdf</a>, <a href="https://arxiv.org/format/2304.08972">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/10.1038/s41598-023-41331-x">10.1038/s41598-023-41331-x <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=M%C3%BCller-Franzes%2C+G">Gustav M眉ller-Franzes</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller-Franzes%2C+F">Fritz M眉ller-Franzes</a>, <a href="/search/cs?searchtype=author&query=Huck%2C+L">Luisa Huck</a>, <a href="/search/cs?searchtype=author&query=Raaff%2C+V">Vanessa Raaff</a>, <a href="/search/cs?searchtype=author&query=Kemmer%2C+E">Eva Kemmer</a>, <a href="/search/cs?searchtype=author&query=Khader%2C+F">Firas Khader</a>, <a href="/search/cs?searchtype=author&query=Arasteh%2C+S+T">Soroosh Tayebi Arasteh</a>, <a href="/search/cs?searchtype=author&query=Nolte%2C+T">Teresa Nolte</a>, <a href="/search/cs?searchtype=author&query=Kather%2C+J+N">Jakob Nikolas Kather</a>, <a href="/search/cs?searchtype=author&query=Nebelung%2C+S">Sven Nebelung</a>, <a href="/search/cs?searchtype=author&query=Kuhl%2C+C">Christiane Kuhl</a>, <a href="/search/cs?searchtype=author&query=Truhn%2C+D">Daniel Truhn</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="2304.08972v1-abstract-short" style="display: inline;"> Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutiona… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.08972v1-abstract-full').style.display = 'inline'; document.getElementById('2304.08972v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.08972v1-abstract-full" style="display: none;"> Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909$\pm$0.069 versus 0.916$\pm$0.067, P<0.001) and on the external testset (0.824$\pm$0.144 versus 0.864$\pm$0.081, P=0.004). Moreover, the average symmetric surface distance was higher (=worse) for nnUNet than for TraBS on the internal (0.657$\pm$2.856 versus 0.548$\pm$2.195, P=0.001) and on the external testset (0.727$\pm$0.620 versus 0.584$\pm$0.413, P=0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.08972v1-abstract-full').style.display = 'none'; document.getElementById('2304.08972v1-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> 18 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sci Rep 13, 14207 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.08325">arXiv:1809.08325</a> <span> [<a href="https://arxiv.org/pdf/1809.08325">pdf</a>, <a href="https://arxiv.org/format/1809.08325">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</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/10.1145/3278532.3278562">10.1145/3278532.3278562 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Rise of Certificate Transparency and Its Implications on the Internet Ecosystem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Scheitle%2C+Q">Quirin Scheitle</a>, <a href="/search/cs?searchtype=author&query=Gasser%2C+O">Oliver Gasser</a>, <a href="/search/cs?searchtype=author&query=Nolte%2C+T">Theodor Nolte</a>, <a href="/search/cs?searchtype=author&query=Amann%2C+J">Johanna Amann</a>, <a href="/search/cs?searchtype=author&query=Brent%2C+L">Lexi Brent</a>, <a href="/search/cs?searchtype=author&query=Carle%2C+G">Georg Carle</a>, <a href="/search/cs?searchtype=author&query=Holz%2C+R">Ralph Holz</a>, <a href="/search/cs?searchtype=author&query=Schmidt%2C+T+C">Thomas C. Schmidt</a>, <a href="/search/cs?searchtype=author&query=W%C3%A4hlisch%2C+M">Matthias W盲hlisch</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="1809.08325v1-abstract-short" style="display: inline;"> In this paper, we analyze the evolution of Certificate Transparency (CT) over time and explore the implications of exposing certificate DNS names from the perspective of security and privacy. We find that certificates in CT logs have seen exponential growth. Website support for CT has also constantly increased, with now 33% of established connections supporting CT. With the increasing deployment o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.08325v1-abstract-full').style.display = 'inline'; document.getElementById('1809.08325v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.08325v1-abstract-full" style="display: none;"> In this paper, we analyze the evolution of Certificate Transparency (CT) over time and explore the implications of exposing certificate DNS names from the perspective of security and privacy. We find that certificates in CT logs have seen exponential growth. Website support for CT has also constantly increased, with now 33% of established connections supporting CT. With the increasing deployment of CT, there are also concerns of information leakage due to all certificates being visible in CT logs. To understand this threat, we introduce a CT honeypot and show that data from CT logs is being used to identify targets for scanning campaigns only minutes after certificate issuance. We present and evaluate a methodology to learn and validate new subdomains from the vast number of domains extracted from CT logged certificates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.08325v1-abstract-full').style.display = 'none'; document.getElementById('1809.08325v1-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> 21 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2018. </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">To be published at ACM IMC 2018</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the ACM Internet Measurement Conference (IMC), pp. 343-349, 2018 </p> 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