Code, documentation, and discussion around the MIMIC-CXR database

[CVPR'25] Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation

Improving Chest X-Ray Report Generation by Leveraging Warm-Starting

Repository to train Latent Diffusion Models on Chest X-ray data (MIMIC-CXR) using MONAI Generative Models

[MICCAI'24] Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation

[ICML 2023] Official repository of paper: Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

[ESWA'26] Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation

Code for reproducing the experiments on large-scale pre-training and transfer learning for the paper "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images" (https://arxiv.org/abs/2106.00116)

[MICCAI 2023, top 14%] Official repository of paper: Distilling BlackBox to Interpretable models for Efficient Transfer Learning

[Bioinformatics'26] Patient-Specific Multimodal Learning with Multi-View Contrastive Alignment for Chest X-ray Report Generation

Codebase for the paper titled 'A Shared Encoder Approach to Multimodal Representation Learning'

Securing Collaborative Medical AI by Using Differential Privacy

Embeddings generation from MIMIC-IV and MIMIC-CXR

Use BART word embedder to do the abstractive summarization NLP task on radiology reports from MIMIC-CXR v2.0.0.

RadioCare: Fighting Inefficiencies in Medical Imaging

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

Chest X-ray classification

Fair, demographically-controllable synthetic medical image generation (chest X-ray & fundus) via hierarchical compositional diffusion.

Pytorch Dataloader for https://mimic.mit.edu

Selective chest X-ray interpretation that knows when to defer — cross-model VLM disagreement + conformal prediction for trustworthy, domain-agnostic automation.