A collection of resources on applications of multi-modal learning in medical imaging.
Foundation models based medical image analysis
[ICML 2026] Official UniMedVL implementation — a unified medical vision-language model for multimodal understanding, medical image generation, and interleaved tasks in a single checkpoint, trained on UniMed-5M (5.6M+ samples).
Awesome radiology report generation and image captioning papers.
[ACL 2023] ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning
[ACL 2025] RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection
[EMNLP 2023 Findings] RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
MedMO: Medical Foundation Model
Code used for the MLMI 2021 paper Clinically Correct Report Generation from Chest X-Rays Using Templates
[EMNLP 2024 Findings] ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation
Medical Report Generation And VQA (Adapting XrayGPT to Any Modality)
This is the official implementation of MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement accepted to IEEE Journal of Biomedical and Health Informatics (J-BHI), 2025.
Official Code Repository of Clinical EEG Language Model (CELM): "Neural Signals Generate Clinical Notes in the Wild". arXiv:
[IJBHI 2024] This is the official implementation of CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation accepted to IEEE Journal of Biomedical and Health Informatics (J-BHI), 2023.
AI-powered Chest X-ray report generation app using VLM (Swin-T5) and LLM (LLaMA-3) for multilingual Q&A and medical education support.
GPT-2 based medical reports generator for X-ray images in Czech.
Medivance.AI is a cutting-edge, all-in-one AI healthcare platform that transforms the way patients, doctors, and healthcare organizations interact with medical data.
Employed Large Language Models to generate accurate wording for the "impression" section in the medical reports
The primary objective of this work is to develop an innovative system capable of providing explainable brain tumor detection.
Resources on the use of multimodal learning in medical imaging.
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