{"total":55,"rows":[{"id":"2107.04649","title":"Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization","created":"2021-07-09","cited_by_count":249,"area":null},{"id":"2401.06148","title":"Artificial Intelligence for Digital and Computational Pathology","created":"2023-12-13","cited_by_count":108,"area":null},{"id":"2102.10553","title":"A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches","created":"2021-02-21","cited_by_count":44,"area":null},{"id":"2102.09099","title":"NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation","created":"2021-02-18","cited_by_count":34,"area":null},{"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,"area":null},{"id":"2310.19656","title":"Domain Generalization in Computational Pathology: Survey and Guidelines","created":"2023-10-30","cited_by_count":29,"area":null},{"id":"2309.15589","title":"Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge","created":"2023-09-27","cited_by_count":25,"area":null},{"id":"2212.06515","title":"AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images","created":"2022-12-13","cited_by_count":24,"area":null},{"id":"2404.15217","title":"Towards Large-Scale Training of Pathology Foundation Models","created":"2024-03-24","cited_by_count":23,"area":null},{"id":"2206.09769","title":"Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology","created":"2022-06-20","cited_by_count":13,"area":null},{"id":"2106.13150","title":"Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology","created":"2021-06-24","cited_by_count":12,"area":null},{"id":"2304.06662","title":"Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions","created":"2023-04-13","cited_by_count":11,"area":null},{"id":"2104.06243","title":"A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers","created":"2021-04-13","cited_by_count":7,"area":null},{"id":"2202.09971","title":"Deep Feature based Cross-slide Registration","created":"2022-02-21","cited_by_count":7,"area":null},{"id":"2304.04142","title":"Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization","created":"2023-04-09","cited_by_count":7,"area":null},{"id":"2402.03325","title":"Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations","created":"2024-01-08","cited_by_count":7,"area":null},{"id":"2208.12587","title":"Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures","created":"2022-08-26","cited_by_count":6,"area":null},{"id":"2310.20427","title":"Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology","created":"2023-10-31","cited_by_count":6,"area":null},{"id":"2203.04308","title":"Breast cancer detection using artificial intelligence techniques: A systematic literature review","created":"2022-03-08","cited_by_count":5,"area":null},{"id":"2206.15274","title":"Augment like there's no tomorrow: Consistently performing neural networks for medical imaging","created":"2022-06-30","cited_by_count":5,"area":null},{"id":"2208.12437","title":"Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge","created":"2022-08-26","cited_by_count":5,"area":null},{"id":"2305.07888","title":"Consistency Regularization for Domain Generalization with Logit Attribution Matching","created":"2023-05-13","cited_by_count":4,"area":null},{"id":"2407.02900","title":"Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2407.12773","title":"OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides","created":"2024-07-17","cited_by_count":4,"area":null},{"id":"2109.01485","title":"MitoDet: Simple and robust mitosis detection","created":"2021-09-02","cited_by_count":3,"area":null},{"id":"2206.01728","title":"A review of machine learning approaches, challenges and prospects for computational tumor pathology","created":"2022-05-31","cited_by_count":3,"area":null},{"id":"2304.08498","title":"Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology","created":"2023-04-15","cited_by_count":3,"area":null},{"id":"2202.08437","title":"Visual attention analysis of pathologists examining whole slide images of Prostate cancer","created":"2022-02-17","cited_by_count":2,"area":null},{"id":"2306.10805","title":"Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer","created":"2023-06-19","cited_by_count":2,"area":null},{"id":"2407.00098","title":"Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images","created":"2024-06-26","cited_by_count":2,"area":null},{"id":"2410.17514","title":"SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images","created":"2024-10-23","cited_by_count":2,"area":null},{"id":"2412.15389","title":"Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervision","created":"2024-12-19","cited_by_count":2,"area":null},{"id":"2502.19123","title":"From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2510.11133","title":"Test-Time Adaptation by Causal Trimming","created":"2025-10-13","cited_by_count":2,"area":null},{"id":"2106.15893","title":"Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification","created":"2021-06-30","cited_by_count":1,"area":null},{"id":"2204.11994","title":"Contrastive learning-based computational histopathology predict differential expression of cancer driver genes","created":"2022-04-25","cited_by_count":1,"area":null},{"id":"2208.13902","title":"Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge","created":"2022-08-29","cited_by_count":1,"area":null},{"id":"2211.00646","title":"Learning Melanocytic Cell Masks from Adjacent Stained Tissue","created":"2022-11-01","cited_by_count":1,"area":null},{"id":"2406.19899","title":"On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images","created":"2024-06-28","cited_by_count":1,"area":null},{"id":"2410.19820","title":"Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2505.12317","title":"Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces","created":"2025-05-18","cited_by_count":1,"area":null},{"id":"2509.02585","title":"Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge","created":"2025-08-28","cited_by_count":1,"area":null},{"id":"2603.15967","title":"A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images","created":"2026-03-16","cited_by_count":1,"area":null},{"id":"2110.14728","title":"Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network","created":"2021-10-27","cited_by_count":0,"area":null},{"id":"2305.05100","title":"Adaptive Domain Generalization for Digital Pathology Images","created":"2023-05-09","cited_by_count":0,"area":null},{"id":"2307.05889","title":"Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2308.03936","title":"ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals","created":"2023-08-07","cited_by_count":0,"area":null},{"id":"2311.06552","title":"Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation","created":"2023-11-11","cited_by_count":0,"area":null},{"id":"2406.02618","title":"Immunocto: a massive immune cell database auto-generated for histopathology","created":"2024-06-03","cited_by_count":0,"area":null},{"id":"2406.15685","title":"PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology Images","created":"2024-06-21","cited_by_count":0,"area":null},{"id":"2503.20653","title":"UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2506.02542","title":"HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2509.03614","title":"Teacher-Student Model for Detecting and Classifying Mitosis in the MIDOG 2025 Challenge","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2511.11286","title":"D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces","created":"2025-11-14","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}