{"total":48,"rows":[{"id":"2107.09044","title":"Just Train Twice: Improving Group Robustness without Training Group Information","created":"2021-07-19","cited_by_count":523,"area":null},{"id":"2103.11251","title":"Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges","created":"2021-03-20","cited_by_count":367,"area":null},{"id":"2203.14960","title":"Domino: Discovering Systematic Errors with Cross-Modal Embeddings","created":"2022-03-24","cited_by_count":156,"area":null},{"id":"2103.12308","title":"IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography","created":"2021-03-23","cited_by_count":72,"area":null},{"id":"2106.11872","title":"Randomness In Neural Network Training: Characterizing The Impact of Tooling","created":"2021-06-22","cited_by_count":59,"area":null},{"id":"2212.04629","title":"Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation","created":"2022-12-09","cited_by_count":59,"area":null},{"id":"2105.05874","title":"The Federated Tumor Segmentation (FeTS) Challenge","created":"2021-05-12","cited_by_count":50,"area":null},{"id":"2302.00785","title":"SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis","created":"2023-02-01","cited_by_count":44,"area":null},{"id":"2107.00520","title":"Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations","created":"2021-06-29","cited_by_count":41,"area":null},{"id":"2312.14223","title":"Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation","created":"2023-12-21","cited_by_count":41,"area":null},{"id":"2209.10015","title":"Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics","created":"2022-09-20","cited_by_count":26,"area":null},{"id":"2309.17430","title":"FACTS: First Amplify Correlations and Then Slice to Discover Bias","created":"2023-09-29","cited_by_count":26,"area":null},{"id":"2308.04431","title":"When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations","created":"2023-08-08","cited_by_count":18,"area":null},{"id":"2211.15231","title":"Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers","created":"2022-11-28","cited_by_count":17,"area":null},{"id":"2404.13815","title":"Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference","created":"2024-04-22","cited_by_count":15,"area":null},{"id":"2406.03323","title":"Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation","created":"2024-06-05","cited_by_count":12,"area":null},{"id":"2302.12893","title":"Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation","created":"2023-02-24","cited_by_count":11,"area":null},{"id":"2307.10404","title":"Interpreting and Correcting Medical Image Classification with PIP-Net","created":"2023-07-19","cited_by_count":11,"area":null},{"id":"2107.05605","title":"Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning","created":"2021-07-12","cited_by_count":10,"area":null},{"id":"2311.04787","title":"Why Do Probabilistic Clinical Models Fail To Transport Between Sites?","created":"2023-11-08","cited_by_count":9,"area":null},{"id":"2211.14673","title":"Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex","created":"2022-11-26","cited_by_count":8,"area":null},{"id":"2312.04712","title":"Error Discovery by Clustering Influence Embeddings","created":"2023-12-07","cited_by_count":8,"area":null},{"id":"2510.02818","title":"Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets","created":"2025-10-03","cited_by_count":8,"area":null},{"id":"2106.06654","title":"Disrupting Model Training with Adversarial Shortcuts","created":"2021-06-12","cited_by_count":7,"area":null},{"id":"2107.09546","title":"Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions","created":"2021-07-20","cited_by_count":7,"area":null},{"id":"2305.02995","title":"Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations","created":"2023-05-04","cited_by_count":7,"area":null},{"id":"2311.04179","title":"On Leakage in Machine Learning Pipelines","created":"2023-11-07","cited_by_count":7,"area":null},{"id":"2204.05205","title":"Rethinking Machine Learning Model Evaluation in Pathology","created":"2022-04-11","cited_by_count":6,"area":null},{"id":"2206.13034","title":"Monitoring Shortcut Learning using Mutual Information","created":"2022-06-27","cited_by_count":6,"area":null},{"id":"2504.16277","title":"DataS^3: Dataset Subset Selection for Specialization","created":"2025-04-22","cited_by_count":6,"area":null},{"id":"2302.04132","title":"Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection","created":"2023-02-08","cited_by_count":4,"area":null},{"id":"2306.08728","title":"Towards trustworthy seizure onset detection using workflow notes","created":"2023-06-14","cited_by_count":4,"area":null},{"id":"2104.10553","title":"Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study","created":"2021-04-21","cited_by_count":3,"area":null},{"id":"2302.13390","title":"MDF-Net for abnormality detection by fusing X-rays with clinical data","created":"2023-02-26","cited_by_count":3,"area":null},{"id":"2106.08445","title":"Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis","created":"2021-06-15","cited_by_count":2,"area":null},{"id":"2406.03984","title":"LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas","created":"2024-06-06","cited_by_count":2,"area":null},{"id":"2509.15107","title":"Limitations of Public Chest Radiography Datasets for Artificial Intelligence: Label Quality, Domain Shift, Bias and Evaluation Challenges","created":"2025-09-18","cited_by_count":2,"area":null},{"id":"2201.09563","title":"Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection","created":"2022-01-24","cited_by_count":1,"area":null},{"id":"2209.03440","title":"Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip","created":"2022-09-07","cited_by_count":1,"area":null},{"id":"2210.09232","title":"Confound-leakage: Confound Removal in Machine Learning Leads to Leakage","created":"2022-10-17","cited_by_count":1,"area":null},{"id":"2509.19671","title":"Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2605.10054","title":"Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2110.12464","title":"Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data","created":"2021-10-24","cited_by_count":0,"area":null},{"id":"2506.10669","title":"PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2508.06943","title":"Class Unbiasing for Generalization in Medical Diagnosis","created":"2025-08-09","cited_by_count":0,"area":null},{"id":"2607.09102","title":"Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging","created":"2026-07-10","cited_by_count":0,"area":null},{"id":"2607.10466","title":"Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2609.35541","title":"Learning the Robustness Mechanism with Bilevel Optimization","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}