LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
hub
arXiv preprint arXiv:2106.14463 , year=
17 Pith papers cite this work. Polarity classification is still indexing.
17
Pith papers citing it
abstract
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In our work, we present RadGraph, a dataset of entities and relations in full-text chest X-ray radiology reports based on a novel information extraction schema we designed to structure radiology reports. We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14,579 entities and 10,889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets. Using these datasets, we train and test a deep learning model, RadGraph Benchmark, that achieves a micro F1 of 0.82 and 0.73 on relation extraction on the MIMIC-CXR and CheXpert test sets respectively. Additionally, we release an inference dataset, which contains annotations automatically generated by RadGraph Benchmark across 220,763 MIMIC-CXR reports (around 6 million entities and 4 million relations) and 500 CheXpert reports (13,783 entities and 9,908 relations) with mappings to associated chest radiographs. Our freely available dataset can facilitate a wide range of research in medical natural language processing, as well as computer vision and multi-modal learning when linked to chest radiographs.
hub tools
citation-role summary
method 2
citation-polarity summary
roles
method 2polarities
use method 2representative citing papers
AnchorDiff is a topology-aware masked diffusion framework with RadGraph anchors and confidence-based rewriting that claims state-of-the-art results on MIMIC-CXR and MIMIC-RG4 for radiology report generation.
MedStruct-S benchmark shows encoder-only models outperform larger decoder-only ones on key-conditioned QA from noisy OCR clinical reports, with fine-tuned large models winning only when scale is ignored.
Neural-MedBench reveals sharp performance drops in state-of-the-art VLMs on reasoning-intensive neurology tasks compared to conventional classification benchmarks, with reasoning failures dominating errors.
AtomiMed is a new modality-agnostic evaluation framework for medical report generation that decomposes reports into hierarchical atomic clinical facts and applies agentic cross-verification to achieve higher correlation with radiologist judgments than n-gram metrics.
DIYHealth Suite introduces a large home-care dataset, DIYHealthGPT model with Hybrid Hyper Low-Rank Adaptation, and DIYHealthBench, claiming SOTA results on 11 tasks over general and medical baselines.
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
ESC-RL improves RL for radiology reports via group-wise evidence-aware rewards (GEAR) and LLM-driven self-correcting preference learning (SPL), reaching state-of-the-art on two chest X-ray datasets.
A dual-loop training strategy with gradient consistency lets vision-language models generate radiology reports from low-quality X-ray images without severe performance loss.
HeartcareGPT proposes Dual Stream Projection Alignment (DSPA) on a structure-aware tokenizer for unified ECG signal-image modeling, supported by Heartcare-400K dataset and Heartcare-Bench.
Lightweight metrics trained on Qwen3-8B and MedGemma-4B using synthetic pairs outperform larger medical LLMs at distinguishing clinical significance in radiology reports while balancing discrimination and robustness.
TIF-GRPO uses integral feedback on pseudo-temporal trajectories to regulate anatomy-aware rewards in RL for clinical faithfulness in volumetric CT analysis.
The UPDP pipeline filters privacy terms and generates de-identified radiology images that preserve diagnostic pathology information, enabling models with competitive disease detection accuracy but reduced identity leakage and improved cross-hospital performance.
HTSC-CIF applies hierarchical task decomposition and cross-modal causal intervention to generate medical reports from images while addressing domain knowledge, alignment, and bias challenges.
KEPIL integrates medical ontologies and a semantic contrastive loss into vision-language models to achieve state-of-the-art prompt-robust zero-shot disease detection in radiology, with reported AUC gains of 6.37% on CheXpert under prompt variations.
MedGemma 1.5 4B reports absolute gains of 11% on 3D MRI classification, 3% on 3D CT, 47% macro F1 on pathology slides, 35% IoU on anatomical localization, and 5-22% on clinical QA tasks over MedGemma 1.
Proposes a human-supervised evidence-linked reference architecture for structured radiology reporting that integrates templates, AI assistance, and interoperability standards like DICOM and FHIR.
citing papers explorer
-
Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
-
AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generation
AnchorDiff is a topology-aware masked diffusion framework with RadGraph anchors and confidence-based rewriting that claims state-of-the-art results on MIMIC-CXR and MIMIC-RG4 for radiology report generation.
-
MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
MedStruct-S benchmark shows encoder-only models outperform larger decoder-only ones on key-conditioned QA from noisy OCR clinical reports, with fine-tuned large models winning only when scale is ignored.
-
Beyond Classification Accuracy: Neural-MedBench and the Need for Deeper Reasoning Benchmarks
Neural-MedBench reveals sharp performance drops in state-of-the-art VLMs on reasoning-intensive neurology tasks compared to conventional classification benchmarks, with reasoning failures dominating errors.
-
AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation
AtomiMed is a new modality-agnostic evaluation framework for medical report generation that decomposes reports into hierarchical atomic clinical facts and applies agentic cross-verification to achieve higher correlation with radiologist judgments than n-gram metrics.
-
DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
DIYHealth Suite introduces a large home-care dataset, DIYHealthGPT model with Hybrid Hyper Low-Rank Adaptation, and DIYHealthBench, claiming SOTA results on 11 tasks over general and medical baselines.
-
CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
-
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning
ESC-RL improves RL for radiology reports via group-wise evidence-aware rewards (GEAR) and LLM-driven self-correcting preference learning (SPL), reaching state-of-the-art on two chest X-ray datasets.
-
Radiology Report Generation for Low-Quality X-Ray Images
A dual-loop training strategy with gradient consistency lets vision-language models generate radiology reports from low-quality X-ray images without severe performance loss.
-
HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding
HeartcareGPT proposes Dual Stream Projection Alignment (DSPA) on a structure-aware tokenizer for unified ECG signal-image modeling, supported by Heartcare-400K dataset and Heartcare-Bench.
-
Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports
Lightweight metrics trained on Qwen3-8B and MedGemma-4B using synthetic pairs outperform larger medical LLMs at distinguishing clinical significance in radiology reports while balancing discrimination and robustness.
-
Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis
TIF-GRPO uses integral feedback on pseudo-temporal trajectories to regulate anatomy-aware rewards in RL for clinical faithfulness in volumetric CT analysis.
-
A Utility-preserving De-identification Pipeline for Cross-hospital Radiology Data Sharing
The UPDP pipeline filters privacy terms and generates de-identified radiology images that preserve diagnostic pathology information, enabling models with competitive disease detection accuracy but reduced identity leakage and improved cross-hospital performance.
-
Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Framework
HTSC-CIF applies hierarchical task decomposition and cross-modal causal intervention to generate medical reports from images while addressing domain knowledge, alignment, and bias challenges.
-
KEPIL: Knowledge-Enhanced Prompt-Image Learning for Prompt-Robust Disease Detection
KEPIL integrates medical ontologies and a semantic contrastive loss into vision-language models to achieve state-of-the-art prompt-robust zero-shot disease detection in radiology, with reported AUC gains of 6.37% on CheXpert under prompt variations.
-
MedGemma 1.5 Technical Report
MedGemma 1.5 4B reports absolute gains of 11% on 3D MRI classification, 3% on 3D CT, 47% macro F1 on pathology slides, 35% IoU on anatomical localization, and 5-22% on clinical QA tasks over MedGemma 1.
-
Evidence-Linked Radiology Reporting: A Human-Supervised Reference Architecture for Structured Imaging Intelligence
Proposes a human-supervised evidence-linked reference architecture for structured radiology reporting that integrates templates, AI assistance, and interoperability standards like DICOM and FHIR.