Papers with Code 아카이브

Medical Report Generation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 3
보존된 평가 기록: 4

Medical report generation (MRG) is a task which focus on training AI to automatically generate professional report according the input image data. This can help clinicians make faster and more accurate decision since the task itself is both time consuming and error prone even for experienced doctors.

Aggfgg

Deep neural network and transformer based architecture are currently the most popular methods for this certain task, however, when we try to transfer out pre-trained model into this certain domain, their performance always degrade.

The following are some of the reasons why RSG is hard for pre-trained models:

Language datasets in a particular domain can sometimes be quite different from the large number of datasets available on the Internet
During the fine-tuning phase, datasets in the medical field are often unevenly distributed

More recently, multi-modal learning and contrastive learning have shown some inspiring results in this field, but it's still challenging and requires further attention.

Here are some additional readings to go deeper on the task:

On the Automatic Generation of Medical Imaging Reports

https://doi.org/10.48550/arXiv.1711.08195
A scoping review of transfer learning research on medical image analysis using ImageNet

https://arxiv.org/abs/2004.13175
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

https://arxiv.org/abs/2004.12150

(Image credit : Transformers in Medical Imaging: A Survey)

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Medical Report Generation

논문

Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

MRGAgents: A Multi-Agent Framework for Improved Medical Report Generation with Med-LVLMs

Towards a HIPAA Compliant Agentic AI System in Healthcare

LVMed-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation

Image-to-Text for Medical Reports Using Adaptive Co-Attention and Triple-LSTM Module

Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

UMIT: Unifying Medical Imaging Tasks via Vision-Language Models

GEMA-Score: Granular Explainable Multi-Agent Score for Radiology Report Evaluation

MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations

PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation

From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine

Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generation

DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning

FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models

A Survey of Medical Vision-and-Language Applications and Their Techniques

The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams

Large Language Model Benchmarks in Medical Tasks

Image-aware Evaluation of Generated Medical Reports

Resource-Efficient Medical Report Generation using Large Language Models

Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning

ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation

CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset

FODA-PG for Enhanced Medical Imaging Narrative Generation: Adaptive Differentiation of Normal and Abnormal Attributes

Medical Report Generation Is A Multi-label Classification Problem

M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation

Automatic Medical Report Generation: Methods and Applications

R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation

ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis

Automated Retinal Image Analysis and Medical Report Generation through Deep Learning

A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning

MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks

MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis

A Survey on Trustworthiness in Foundation Models for Medical Image Analysis

Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation

CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report Generation