{"total":59,"rows":[{"id":"2411.19666","title":"Multimodal Whole Slide Foundation Model for Pathology","created":"2024-11-29","cited_by_count":162,"area":null},{"id":"2410.11761","title":"SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding","created":"2024-10-15","cited_by_count":57,"area":"mllm"},{"id":"2505.20202","title":"PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology","created":"2025-05-26","cited_by_count":24,"area":null},{"id":"2412.13126","title":"Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosis","created":"2024-12-17","cited_by_count":21,"area":"mllm"},{"id":"2411.14743","title":"FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification","created":"2024-11-22","cited_by_count":20,"area":null},{"id":"2504.04045","title":"A Survey of Pathology Foundation Model: Progress and Future Directions","created":"2025-04-05","cited_by_count":16,"area":null},{"id":"2411.07660","title":"HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification","created":"2024-11-12","cited_by_count":13,"area":null},{"id":"2507.17303","title":"A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model","created":"2025-07-23","cited_by_count":12,"area":"lr"},{"id":"2408.14496","title":"A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models","created":"2024-08-23","cited_by_count":9,"area":"mllm"},{"id":"2503.09091","title":"Multi-Modal Foundation Models for Computational Pathology: A Survey","created":"2025-03-12","cited_by_count":9,"area":null},{"id":"2502.04946","title":"SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers","created":"2025-02-07","cited_by_count":8,"area":null},{"id":"2506.09095","title":"Foundation Models in Medical Imaging: A Review and Outlook","created":"2025-06-10","cited_by_count":8,"area":null},{"id":"2506.13063","title":"PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue","created":"2025-06-16","cited_by_count":8,"area":null},{"id":"2508.02419","title":"Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens","created":"2025-08-04","cited_by_count":8,"area":"lr"},{"id":"2407.17734","title":"Cost-effective Instruction Learning for Pathology Vision and Language Analysis","created":"2024-07-25","cited_by_count":7,"area":"mllm"},{"id":"2601.05148","title":"Atlas 2 -- Foundation models for clinical deployment","created":"2026-01-08","cited_by_count":7,"area":null},{"id":"2412.00651","title":"Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics","created":"2024-12-01","cited_by_count":5,"area":null},{"id":"2505.10993","title":"Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2411.15076","title":"RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency","created":"2024-11-22","cited_by_count":4,"area":null},{"id":"2505.15687","title":"Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning","created":"2025-05-21","cited_by_count":4,"area":null},{"id":"2508.01602","title":"Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment","created":"2025-08-03","cited_by_count":4,"area":null},{"id":"2503.00374","title":"MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention","created":"2025-03-01","cited_by_count":3,"area":null},{"id":"2512.23545","title":"PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis","created":"2025-12-29","cited_by_count":3,"area":"mllm"},{"id":"2508.20851","title":"PathMR: Multimodal Visual Reasoning for Interpretable Pathology Diagnosis","created":"2025-08-28","cited_by_count":2,"area":"lr"},{"id":"2509.12600","title":"A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction","created":"2025-09-16","cited_by_count":2,"area":null},{"id":"2505.15504","title":"Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2506.19681","title":"Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2507.10250","title":"DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology","created":"2025-07-14","cited_by_count":1,"area":null},{"id":"2509.01071","title":"A Unified Low-level Foundation Model for Enhancing Pathology Image Quality","created":"2025-09-01","cited_by_count":1,"area":null},{"id":"2509.11526","title":"Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis","created":"2025-09-15","cited_by_count":1,"area":null},{"id":"2512.17726","title":"MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Image","created":"2025-12-19","cited_by_count":1,"area":null},{"id":"2602.14010","title":"A Deployment-Friendly Foundational Framework for Efficient Computational Pathology","created":"2026-02-15","cited_by_count":1,"area":null},{"id":"2604.03635","title":"A Generative Foundation Model for Multimodal Histopathology","created":"2026-04-04","cited_by_count":1,"area":null},{"id":"2606.20677","title":"Democratizing and accelerating AI-driven pathology research through agentic intelligence","created":"2026-06-12","cited_by_count":1,"area":"agents"},{"id":"2607.04020","title":"Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology","created":"2026-07-04","cited_by_count":1,"area":null},{"id":"2501.18170","title":"Continually Evolved Multimodal Foundation Models for Cancer Prognosis","created":"2025-01-30","cited_by_count":0,"area":null},{"id":"2505.12711","title":"Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2505.24141","title":"The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2507.18433","title":"DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis","created":"2025-07-24","cited_by_count":0,"area":"mllm"},{"id":"2508.15904","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2510.23224","title":"Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2512.14594","title":"LLM-driven Knowledge Enhancement for Multimodal Cancer Survival Prediction","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2602.02558","title":"PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationships","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.13944","title":"Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology","created":"2026-02-15","cited_by_count":0,"area":null},{"id":"2603.03030","title":"BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2603.13377","title":"Deep Learning for BioImaging: What Are We Really Learning?","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2603.13787","title":"Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.04999","title":"PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities","created":"2026-04-05","cited_by_count":0,"area":null},{"id":"2605.00902","title":"Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data","created":"2026-04-28","cited_by_count":0,"area":null},{"id":"2605.31504","title":"When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.28697","title":"Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation","created":"2026-06-27","cited_by_count":0,"area":null},{"id":"2607.25497","title":"A Distributional Robustness Margin For Pathology Foundation Models","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2608.01356","title":"Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2608.01370","title":"Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2608.24688","title":"A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2608.27278","title":"Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.02118","title":"Synergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathology","created":"2026-09-02","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":"2609.35943","title":"HERO: Histology Encoder for Robust Representation in Oncology","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}