{"list":"other","total":55,"rows":[{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"2006.09882","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","created":"2020-06-17","cited_by_count":2857},{"id":"2508.10104","title":"DINOv3","created":"2025-08-13","cited_by_count":1414},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"1802.04712","title":"Attention-based Deep Multiple Instance Learning","created":"2018-02-13","cited_by_count":1095},{"id":"2111.07832","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","created":"2021-11-15","cited_by_count":903},{"id":"2309.16588","title":"Vision Transformers Need Registers","created":"2023-09-28","cited_by_count":871},{"id":"2206.14486","title":"Beyond neural scaling laws: beating power law scaling via data pruning","created":"2022-06-29","cited_by_count":528},{"id":"1806.03962","title":"Rotation Equivariant CNNs for Digital Pathology","created":"2018-06-08","cited_by_count":422},{"id":"1812.06499","title":"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images","created":"2018-12-16","cited_by_count":388},{"id":"1902.10178","title":"Unmasking Clever Hans Predictors and Assessing What Machines Really Learn","created":"2019-02-26","cited_by_count":365},{"id":"2303.09540","title":"SemDeDup: Data-efficient learning at web-scale through semantic deduplication","created":"2023-03-16","cited_by_count":303},{"id":"2309.16671","title":"Demystifying CLIP Data","created":"2023-09-28","cited_by_count":270},{"id":"1902.06543","title":"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology","created":"2019-02-18","cited_by_count":173},{"id":"2411.19666","title":"Multimodal Whole Slide Foundation Model for Pathology","created":"2024-11-29","cited_by_count":162},{"id":"2408.00738","title":"Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology","created":"2024-08-01","cited_by_count":161},{"id":"2011.13971","title":"Self supervised contrastive learning for digital histopathology","created":"2020-11-27","cited_by_count":146},{"id":"2111.04740","title":"BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images","created":"2021-11-08","cited_by_count":132},{"id":"2003.10778","title":"PanNuke Dataset Extension, Insights and Baselines","created":"2020-03-24","cited_by_count":114},{"id":"2406.16192","title":"HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis","created":"2024-06-23","cited_by_count":107},{"id":"2205.10287","title":"On the SDEs and Scaling Rules for Adaptive Gradient Algorithms","created":"2022-05-20","cited_by_count":104},{"id":"2405.10254","title":"PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology","created":"2024-05-16","cited_by_count":85},{"id":"2409.09173","title":"Phikon-v2, A large and public feature extractor for biomarker prediction","created":"2024-09-13","cited_by_count":74},{"id":"2502.06750","title":"Accelerating Data Processing and Benchmarking of AI Models for Pathology","created":"2025-02-10","cited_by_count":65},{"id":"2101.12355","title":"A Petri Dish for Histopathology Image Analysis","created":"2021-01-29","cited_by_count":64},{"id":"2407.15362","title":"A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model","created":"2024-07-22","cited_by_count":59},{"id":"1808.05896","title":"Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks","created":"2018-08-17","cited_by_count":57},{"id":"2406.05074","title":"Hibou: A Family of Foundational Vision Transformers for Pathology","created":"2024-06-07","cited_by_count":57},{"id":"2405.15613","title":"Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach","created":"2024-05-24","cited_by_count":50},{"id":"2401.04079","title":"RudolfV: A Foundation Model by Pathologists for Pathologists","created":"2024-01-08","cited_by_count":41},{"id":"2501.18055","title":"Current Pathology Foundation Models are unrobust to Medical Center Differences","created":"2025-01-29","cited_by_count":39},{"id":"2501.16239","title":"Distilling foundation models for robust and efficient models in digital pathology","created":"2025-01-27","cited_by_count":36},{"id":"2206.12694","title":"RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization","created":"2022-06-25","cited_by_count":32},{"id":"2310.07033","title":"Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images","created":"2023-10-10","cited_by_count":31},{"id":"2303.13110","title":"OCELOT: Overlapped Cell on Tissue Dataset for Histopathology","created":"2023-03-23","cited_by_count":29},{"id":"2408.08041","title":"The Clever Hans Effect in Unsupervised Learning","created":"2024-08-15","cited_by_count":24},{"id":"2504.05186","title":"Training state-of-the-art pathology foundation models with orders of magnitude less data","created":"2025-04-07","cited_by_count":21},{"id":"2411.05489","title":"Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study","created":"2024-11-08","cited_by_count":18},{"id":"2405.07905","title":"PLUTO: Pathology-Universal Transformer","created":"2024-05-13","cited_by_count":17},{"id":"2503.02876","title":"SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models","created":"2025-03-04","cited_by_count":13},{"id":"2301.02608","title":"An interpretable machine learning system for colorectal cancer diagnosis from pathology slides","created":"2023-01-06","cited_by_count":9},{"id":"2410.06723","title":"Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts","created":"2024-10-09","cited_by_count":9},{"id":"2507.07860","title":"THUNDER: Tile-level Histopathology image UNDERstanding benchmark","created":"2025-07-10","cited_by_count":9},{"id":"2406.14866","title":"AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics","created":"2024-06-21","cited_by_count":8},{"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},{"id":"2506.17140","title":"MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification","created":"2025-06-20","cited_by_count":7},{"id":"2507.22092","title":"Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss","created":"2025-07-29","cited_by_count":7},{"id":"2601.05148","title":"Atlas 2 -- Foundation models for clinical deployment","created":"2026-01-08","cited_by_count":7},{"id":"2502.16889","title":"Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models","created":"2025-02-24","cited_by_count":4},{"id":"2503.18709","title":"Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology","created":"2025-03-24","cited_by_count":4},{"id":"2511.02826","title":"PLUTO-4: Frontier Pathology Foundation Models","created":"2025-11-04","cited_by_count":2},{"id":"2609.13567","title":"Causal multi-modal AI for personalized chemosensitivity prediction","created":"2026-09-11","cited_by_count":2}],"next_cursor":null}