Sparse Autoencoders for Interpretable Medical Image Representation Learning
稀疏自编码器用于可解释的医学图像表示学习
Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa, Sergios Gatidis
机构
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Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福大学人工智能医学与成像中心）
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Department of Radiology, Stanford University（斯坦福大学放射科）
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Chair of AI in Healthcare and Medicine, Technical University of Munich（慕尼黑技术大学人工智能医疗与医学主任）
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TUM University Hospital, Munich, Germany（慕尼黑技术大学医院）
A Novel Framework using Intuitionistic Fuzzy Logic with U-Net and U-Net++ Architecture: A case Study of MRI Bain Image Segmentation
一种利用直觉模糊逻辑与U-Net和U-Net++架构的新型框架：MRI脑图像分割的案例研究
Hanuman Verma, Kiho Im, Akshansh Gupta, M. Tanveer
机构
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Department of Mathematics, Bareilly College, Bareilly (MJP Rohilkhand University), Uttar Pradesh, 243005, India（巴里利学院数学系，巴里利（MJP罗希兰德大学），乌塔尔普拉德什，243005，印度）
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Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA and Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA also with Department of Pediatrics, Harvard Medical School, Boston, MA, USA（波士顿儿童医院胎儿和新生儿神经影像与发育科学中心，哈佛医学院，波士顿，马萨诸塞州02115，美国；波士顿儿童医院新生儿医学科，哈佛医学院，波士顿，马萨诸塞州02115，美国；也与哈佛医学院儿科系，波士顿，马萨诸塞州，美国）
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National Institute of Science Communication and Policy Research, New Delhi, 110012, India（国家科学传播与政策研究所，新德里，110012，印度）
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Department of Mathematics, Indian Institute of Technology Indore, Indore (M.P.)-453552, India（印度理工学院印度尔数学系，印度尔（马哈拉施特拉邦）-453552，印度）
BarcodeBERT: Transformers for Biodiversity Analysis
BarcodeBERT：用于生物多样性分析的Transformer模型
Pablo Millan Arias, Niousha Sadjadi, Monireh Safari, ZeMing Gong, Austin T. Wang, Joakim Bruslund Haurum, Iuliia Zarubiieva, Dirk Steinke, Lila Kari, Angel X. Chang, Scott C. Lowe, Graham W. Taylor
机构
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University of Waterloo（滑铁卢大学）
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Simon Fraser University（西蒙弗雷泽大学）
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Aalborg University（奥胡斯大学）
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Pioneer Centre for AI（先锋人工智能中心）
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University of Guelph（格雷厄姆大学）
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Vector Institute（向量研究所）
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Alberta Machine Intelligence Institute (Amii)（阿尔伯塔人工智能研究所）
CommentsMain text: 14 pages, Total: 23 pages, 10 figures, formerly accepted at the 4th Workshop on Self-Supervised Learning: Theory and Practice (NeurIPS 2023)
Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
Doctor-R1：通过经验代理强化学习掌握临床探究
Yunghwei Lai, Kaiming Liu, Ziyue Wang, Weizhi Ma, Yang Liu
机构
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Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）
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College of AI, Tsinghua University（人工智能学院，清华大学）
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Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）
Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention
增强肾肿瘤恶性预测：基于自动3D CT器官聚焦注意力的深度学习
Zhengkang Fan, Chengkun Sun, Russell Terry, Jie Xu, Longin Jan Latecki
机构
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1 Department of Health Outcomes \& Biomedical Informatics, University of Florida, Gainesville, FL, USA 2 Department of Urology, University of Florida, Gainesville, FL, USA 3 Department of Computer \& Information Sciences, Temple University, Philadelphia, PA, USA
Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes
现成的图像到图像模型就是对抗图像保护方案所需的一切
Xavier Pleimling, Sifat Muhammad Abdullah, Gunjan Balde, Peng Gao, Mainack Mondal, Murtuza Jadliwala, Bimal Viswanath
机构
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University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
专题命中
仓库级理解
：repository(abstract)；分类 cs.AI
AI总结
现成的图像到图像模型可有效对抗图像保护方案，揭示其存在广泛漏洞，需建立更坚固的防御措施。
CommentsThis work has been accepted for publication at the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). The final version will be available on IEEE Xplore. To IEEE SaTML 2026
Satellite-Based Detection of Looted Archaeological Sites Using Machine Learning
基于卫星的考古遗址盗掘检测使用机器学习
Girmaw Abebe Tadesse, Titien Bartette, Andrew Hassanali, Allen Kim, Jonathan Chemla, Andrew Zolli, Yves Ubelmann, Caleb Robinson, Inbal Becker-Reshef, Juan Lavista Ferres
机构
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Microsoft AI for Good Research Lab（微软AI for Good研究实验室）
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Iconem
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Planet Labs PBC（Planet Labs公司）
CommentsAccepted in the Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE), Seoul, Korea. Supported by EU's Horizon 2021 research and innovation programme under grant agreement no. 101070599 (SecOPERA)
Journal ref2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 4070-4073
