{"total":18,"rows":[{"id":"2207.12268","title":"What is Healthy? Generative Counterfactual Diffusion for Lesion Localization","created":"2022-07-25","cited_by_count":52,"area":null},{"id":"2108.12043","title":"Learning Disentangled Representations in the Imaging Domain","created":"2021-08-26","cited_by_count":46,"area":null},{"id":"2107.02622","title":"Detecting Outliers with Poisson Image Interpolation","created":"2021-07-06","cited_by_count":44,"area":null},{"id":"2103.03158","title":"A Structural Causal Model for MR Images of Multiple Sclerosis","created":"2021-03-04","cited_by_count":21,"area":null},{"id":"2401.16363","title":"Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET","created":"2024-01-29","cited_by_count":10,"area":null},{"id":"2506.16213","title":"CF-Seg: Counterfactuals meet Segmentation","created":"2025-06-19","cited_by_count":10,"area":null},{"id":"2203.11132","title":"Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare","created":"2022-03-21","cited_by_count":9,"area":null},{"id":"2308.09026","title":"LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation","created":"2023-08-17","cited_by_count":7,"area":null},{"id":"2407.01561","title":"Synthetic Data in Radiological Imaging: Current State and Future Outlook","created":"2024-05-08","cited_by_count":7,"area":null},{"id":"2203.07815","title":"Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification","created":"2022-03-15","cited_by_count":6,"area":null},{"id":"2208.02135","title":"Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images","created":"2022-08-03","cited_by_count":5,"area":null},{"id":"2103.12328","title":"Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval","created":"2021-03-23","cited_by_count":4,"area":null},{"id":"2106.15345","title":"Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image","created":"2021-06-24","cited_by_count":2,"area":null},{"id":"2203.15347","title":"Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis","created":"2022-03-29","cited_by_count":2,"area":null},{"id":"2608.28787","title":"Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI","created":"2026-08-28","cited_by_count":1,"area":null},{"id":"2101.06468","title":"Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation","created":"2021-01-16","cited_by_count":0,"area":null},{"id":"2105.00463","title":"Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information","created":"2021-05-02","cited_by_count":0,"area":null},{"id":"2212.06299","title":"Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map","created":"2022-12-13","cited_by_count":0,"area":null}],"next_cursor":null}