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<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/2502.09662">arXiv:2502.09662</a> <span> [<a href="https://arxiv.org/pdf/2502.09662">pdf</a>, <a href="https://arxiv.org/format/2502.09662">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey 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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jiang%2C+H">Hao Jiang</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+C">Cheng Jin</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+H">Huangjing Lin</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+Y">Yanning Zhou</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xi Wang</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+J">Jiabo Ma</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+L">Li Ding</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+J">Jun Hou</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+R">Runsheng Liu</a>, <a href="/search/eess?searchtype=author&query=Chai%2C+Z">Zhizhong Chai</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+L">Luyang Luo</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+H">Huijuan Shi</a>, <a href="/search/eess?searchtype=author&query=Qian%2C+Y">Yinling Qian</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Q">Qiong Wang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+C">Changzhong Li</a>, <a href="/search/eess?searchtype=author&query=Han%2C+A">Anjia Han</a>, <a href="/search/eess?searchtype=author&query=Chan%2C+R+C+K">Ronald Cheong Kin Chan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Hao Chen</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="2502.09662v1-abstract-short" style="display: inline;"> Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening solution, current systems struggle with generalizability in complex clinical scenarios. To address this issue, we introduced Smart-CCS, a generalizable Cervical Cancer Screening paradigm based on pretraining and adaptation to create robust and general… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09662v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09662v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09662v1-abstract-full" style="display: none;"> Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening solution, current systems struggle with generalizability in complex clinical scenarios. To address this issue, we introduced Smart-CCS, a generalizable Cervical Cancer Screening paradigm based on pretraining and adaptation to create robust and generalizable screening systems. To develop and validate Smart-CCS, we first curated a large-scale, multi-center dataset named CCS-127K, which comprises a total of 127,471 cervical cytology whole-slide images collected from 48 medical centers. By leveraging large-scale self-supervised pretraining, our CCS models are equipped with strong generalization capability, potentially generalizing across diverse scenarios. Then, we incorporated test-time adaptation to specifically optimize the trained CCS model for complex clinical settings, which adapts and refines predictions, improving real-world applicability. We conducted large-scale system evaluation among various cohorts. In retrospective cohorts, Smart-CCS achieved an overall area under the curve (AUC) value of 0.965 and sensitivity of 0.913 for cancer screening on 11 internal test datasets. In external testing, system performance maintained high at 0.950 AUC across 6 independent test datasets. In prospective cohorts, our Smart-CCS achieved AUCs of 0.947, 0.924, and 0.986 in three prospective centers, respectively. Moreover, the system demonstrated superior sensitivity in diagnosing cervical cancer, confirming the accuracy of our cancer screening results by using histology findings for validation. Interpretability analysis with cell and slide predictions further indicated that the system's decision-making aligns with clinical practice. Smart-CCS represents a significant advancement in cancer screening across diverse clinical contexts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09662v1-abstract-full').style.display = 'none'; document.getElementById('2502.09662v1-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> 12 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.18449">arXiv:2407.18449</a> <span> [<a href="https://arxiv.org/pdf/2407.18449">pdf</a>, <a href="https://arxiv.org/format/2407.18449">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> <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"> Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ma%2C+J">Jiabo Ma</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Z">Zhengrui Guo</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+F">Fengtao Zhou</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yihui Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yingxue Xu</a>, <a href="/search/eess?searchtype=author&query=Cai%2C+Y">Yu Cai</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Zhengjie Zhu</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+C">Cheng Jin</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+Y">Yi Lin</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+X">Xinrui Jiang</a>, <a href="/search/eess?searchtype=author&query=Han%2C+A">Anjia Han</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+L">Li Liang</a>, <a href="/search/eess?searchtype=author&query=Chan%2C+R+C+K">Ronald Cheong Kin Chan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jiguang Wang</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+K">Kwang-Ting Cheng</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Hao Chen</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="2407.18449v2-abstract-short" style="display: inline;"> Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial for the success in various downstream clinical tasks. However, current foundation models have only been evaluated on a limited type and number of tasks, leaving their generalization ability and overall performance unclear.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18449v2-abstract-full').style.display = 'inline'; document.getElementById('2407.18449v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18449v2-abstract-full" style="display: none;"> Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial for the success in various downstream clinical tasks. However, current foundation models have only been evaluated on a limited type and number of tasks, leaving their generalization ability and overall performance unclear. To address this gap, we established a most comprehensive benchmark to evaluate the performance of off-the-shelf foundation models across six distinct clinical task types, encompassing a total of 39 specific tasks. Our findings reveal that existing foundation models excel at certain task types but struggle to effectively handle the full breadth of clinical tasks. To improve the generalization of pathology foundation models, we propose a unified knowledge distillation framework consisting of both expert and self knowledge distillation, where the former allows the model to learn from the knowledge of multiple expert models, while the latter leverages self-distillation to enable image representation learning via local-global alignment. Based on this framework, a Generalizable Pathology Foundation Model (GPFM) is pretrained on a large-scale dataset consisting of 190 million images from around 86,000 public H&E whole slides across 34 major tissue types. Evaluated on the established benchmark, GPFM achieves an impressive average rank of 1.36, with 29 tasks ranked 1st, while the the second-best model, UNI, attains an average rank of 2.96, with only 4 tasks ranked 1st. The superior generalization of GPFM demonstrates its exceptional modeling capabilities across a wide range of clinical tasks, positioning it as a new cornerstone for feature representation in CPath. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18449v2-abstract-full').style.display = 'none'; document.getElementById('2407.18449v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> I.2.10 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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