{"total":28,"rows":[{"id":"2512.21331","title":"TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning","created":"2025-12-24","cited_by_count":6,"area":null},{"id":"2602.21637","title":"CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis","created":"2026-02-25","cited_by_count":6,"area":null},{"id":"2603.06658","title":"ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging","created":"2026-03-01","cited_by_count":6,"area":null},{"id":"2508.16085","title":"Ensemble learning of pathology foundation models for precision oncology","created":"2025-08-22","cited_by_count":4,"area":null},{"id":"2603.22198","title":"Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning","created":"2026-03-23","cited_by_count":4,"area":null},{"id":"2602.03998","title":"AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology","created":"2026-02-03","cited_by_count":3,"area":null},{"id":"2602.19005","title":"GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound","created":"2026-02-22","cited_by_count":3,"area":null},{"id":"2603.08328","title":"Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology","created":"2026-03-09","cited_by_count":2,"area":null},{"id":"2508.13482","title":"Cross-Cancer Knowledge Transfer in WSI-based Prognosis Prediction","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2510.25759","title":"Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning","created":"2025-10-29","cited_by_count":1,"area":null},{"id":"2606.20027","title":"QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging","created":"2026-06-18","cited_by_count":1,"area":null},{"id":"2509.20923","title":"Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2512.14540","title":"CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learning","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2601.11691","title":"Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype","created":"2026-01-16","cited_by_count":0,"area":null},{"id":"2602.01951","title":"Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.09953","title":"Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2604.12100","title":"PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2605.17449","title":"Spatial Blindness in Whole-Slide Multiple Instance Learning","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2605.17456","title":"GCE-MIL: Faithful and Recoverable Evidence for Multiple Instance Learning in Whole-Slide Imaging","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2605.24253","title":"CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.06458","title":"In-Context Multiple Instance Learning","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2606.10778","title":"From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2607.11257","title":"LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.14703","title":"Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2608.22066","title":"ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology","created":"2026-08-22","cited_by_count":0,"area":null},{"id":"2609.33178","title":"Can Protein-Derived Knowledge Improve Pathology Foundation Models?","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.34451","title":"Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.17233","title":"Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}