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arXiv Machine Learning
Jun 5

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.

By Yanqing Luo (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Julius Hense (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Niklas Preni{\ss}l (Institute of Pathology, Charit\'e Universit\"atsmedizin, Berlin, Germany, Berlin Institute of Health at Charit\'e -- Universit\"atsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charit\'e Digital Clinician Scientist Program, Berlin, Germany), Andreas Mock (Institute of Pathology, Ludwig Maximilian University of Munich, Munich, Germany, Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany, NCT Heidelberg, Heidelberg, Germany, German Cancer Consortium), Klaus-Robert M\"uller (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max-Planck Institute for Informatics, Saarbr\"ucken, Germany), Thomas Schnake (Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Acceleration Consortium, University of Toronto, Canada), Mina Jamshidi Idaji (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany)
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arXiv Computer Vision
6d ago

Refining Cytology Predictions with Conditional Random Fields

The paper introduces CytoCRF, a conditional random field framework tailored for cytology images. It adapts pairwise terms to focus on chromatin and cytology-specific staining and enriches neighborhood information by combining multiple backbone models. Across ten cytology datasets, CytoCRF surpasses existing CRF methods at all annotation budgets, achieving up to +13.6 percentage points over the best baseline and +33.7 over zero‑shot performance with only 50 annotations.

By Manon Dausort, Tiffanie Godelaine, Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Beno\^it Macq
llmsmultimodal
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arXiv AI
Sep 15

Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization

The paper introduces UdonCare, a hierarchy‑pruning method that iteratively partitions patients into latent domains using medical ontologies, aiming to improve domain generalization in clinical prediction tasks. It addresses challenges of missing domain labels and lack of clinical insight by discovering hierarchy‑grounded patient domains. Experiments on MIMIC‑III, MIMIC‑IV, and eICU datasets show UdonCare outperforms eight baseline methods across four prediction tasks with significant domain gaps.

By Pengfei Hu, Xiaoxue Han, Fei Wang, Yue Ning
efficiency
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arXiv AI
Jul 3

Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment

arXiv:2511. 05150v2 Announce Type: replace-cross Abstract: Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment.

By Jingsong Liu, Han Li, Zhengyang Xu, Franz-Leonard Klaus, Fabian St\"ogbauer, Shihui Zu, Weiwei Zhou, Atsuko Kasajima, Felix Schicktanz, Alexander Muckenhuber, Julius Shakhtour, Jiale Yu, Tiannan Zheng, Xun Ma, Maggie Wang, Christian Grashei, Bao Li, Guiyang Jiang, Hongming Xu, Shaohua Kevin Zhou, Nassir Navab, Peter J. Sch\"uffler
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