The paper introduces a multiscale evidential learning framework for cervical cytology that models disease progression along an ordinal continuum using clinically grounded soft anchors. It encodes nuclear, cytoplasmic, and neighborhood cellular features separately with a shared DINOv2 backbone, then transforms calibrated predictions into Dempster–Shafer belief masses, fusing them with reliability discounting to explicitly handle conflicting or insufficient evidence. The resulting belief state enables interpretable routing that distinguishes confidently classified cases from those requiring further review, and demonstrates consistent generalization across two public cytology datasets with uncertainty correlating strongly with diagnostic difficulty.
By Ajeet Kumar Singh, Nehal Mishra, Anurag Tripathi, Santanu Chaudhury, Dorien Herremans
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)
arXiv:2609.17545v1 Announce Type: new
Abstract: Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed along...
By Nisreen Albzour, Sarah S. Lam
arXiv:2609.09189v1 Announce Type: cross
Abstract: High classification accuracy alone is insufficient for clinical image analysis, where calibrated confidence and reliable uncertainty estimates are es...
By Nisreen Albzour, Sarah S. Lam
arXiv:2609.31314v1 Announce Type: new
Abstract: Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving acros...
By Wenjie Li, Zishan Xu, Jinyang Huang, Zhengxin Nie, Shichao Kan, Yixiong Liang
TAM-Chain is a multi‑scale thyroid cytology classification framework that uses Absorbing Markov Chains and Shannon Entropy to quantify uncertainty and dynamically decide when to stop processing and refer to a specialist. It processes images at 10×, 20×, and 40× magnifications, achieving a Macro F1 score of 0.9741 on an internal test set with a 0 % false‑negative rate, and maintains a Macro F1 of 0.7026 on an external validation set with severe domain shift. The method outperforms single‑magnification baselines by adaptively adjusting stopping steps and triggering specialist referrals, thereby reducing critical diagnostic errors.
By Hai Pham Ngoc
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
Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content. Such control is essential because clinical scenarios diverge: emergency triage prioritizes sensitivity to reduce missed findings, whereas confirmatory interpretation emphasizes specificity to limit unnecessary interventions.
arXiv:2605.07785v3 Announce Type: replace
Abstract: Concept Bottleneck Models (CBMs) in medical imaging aim to improve model interpretability by predicting intermediate clinical concepts before final...
By Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
CTSpinoPelvic1K is a new dataset that unifies spine, pelvis, ribs, and femora annotations from two prior collections (CTSpine1K and CTPelvic1K) into a single coordinate frame. It contains 802 abdominal‑pelvic CT scans with per‑level rib and femur labels, anchored on the lowest rib‑bearing vertebra and S1, and includes explicit classes for lumbosacral transitional vertebrae (L6, T13, separate S1, lumbar ribs). The data were validated for geometric consistency, rib‑vertebra incidence, and spinopelvic measurements, and are available as NIfTI image/label pairs with a loader and stratified cross‑validation splits.
By Gregory Schwing, Ashley Schehr, Annika Tekumulla, Margret Khoushi, Ryan Christian, Dane Hubers, Faris Mahjoub, Hassan Saad, Mia Sooch, Sathyagopal Siddapureddy, Michael McLellan, Jerick Kim, Miraziz Ismoilov, Nizar Alnabahneh
Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluster assignments, witho...
arXiv:2609.12835v1 Announce Type: new
Abstract: Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories...
By Afshin Bozorgpour, Peter Sch\"uffler, Edgar Jost, Dorit Merhof