Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for medical image analysis use predefined template databases or ignore hierarchical nature of medical report generation. |
| Approach: | They propose a hierarchical retrieval mechanism to extract both report and sentence-level templates for clinically accurate report generation. |
| Outcome: | The proposed model extracts both report and sentence-level templates for clinically accurate report generation. |
Similar Papers
On the Automatic Generation of Medical Imaging Reports (P18-1)
Copied to clipboard
| Challenge: | a complete medical imaging report contains multiple heterogeneous forms of information, including findings and tags . abnormal regions in medical images are difficult to identify and the reports are typically long, containing multiple sentences. |
| Approach: | They propose a multi-task learning framework which predicts tags and generates paragraphs for abnormal regions in medical images. |
| Outcome: | The proposed framework can generate long paragraphs on two publicly available datasets. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
Copied to clipboard
Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
Automated Generation of Accurate & Fluent Medical X-ray Reports (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. |
| Approach: | They propose to automate the generation of medical reports from chest X-ray image inputs . medical reports are the primary medium, which physicians communicate findings from scans - authors say . |
| Outcome: | The proposed method achieves fluency and clinical accuracy on common metrics. |
Normal-Abnormal Decoupling Memory for Medical Report Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for capturing nuanced visual information are prone to data bias and noise. |
| Approach: | They propose a normal-abnormal semantic decoupling network that utilizes abnormal pattern memory to optimize visual extraction through the extraction of abnormal semantics from the reports. |
| Outcome: | The proposed approach surpasses the current state-of-the-art methods on the benchmark MIMIC-CXR and shows excellent performance on the same dataset. |
Multimodal Dual-Path Decoding for Medical Report Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Current methods for radiology report generation rely on encoder-decoder based frameworks that fail to integrate multimodal clinical evidence with domain-specific knowledge. |
| Approach: | They propose a multimodal dual-path framework that synergistically integrates large vision-language models and large language models for radiology report generation. |
| Outcome: | The proposed framework improves on the public MIMIC-CXR benchmark and shows that it is superior to state-of-the-art models. |
A Self-training Framework for Automated Medical Report Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Medical report generation is an important medical artificial intelligence task. |
| Approach: | They propose a framework for medical report generation that exploits unlabeled medical images and a reference-free evaluation metric. |
| Outcome: | The proposed framework performs better than previous fully-supervised models trained on entire training data. |
MediVLM: A Vision Language Model for Radiology Report Generation from Medical Images (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for radiology report generation from medical images are incomplete and inconsistent, fail to focus on informative regions within an image and impose strong annotation assumptions for model training. |
| Approach: | They propose a vision language model (VLM) for radiology report generation from medical images that uses a pre-trained object detector to extract the salient anatomical regions from images, an image encoder, a text encoder and a transformer based decoder to generate the final report. |
| Outcome: | The proposed model generates radiology reports even when no reports are available for training. |
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation (2024.findings-acl)
Copied to clipboard
| Challenge: | Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. |
| Approach: | They propose a Fact Extractor that leverages large language models to extract factual statements from radiology reports. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods in sentence ranking, natural language inference, and label extraction tasks. |
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)
Copied to clipboard
| Challenge: | Medical large vision-language models suffer from factual inaccuracies and unreliable outputs. |
| Approach: | They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources. |
| Outcome: | The proposed framework improves Med-LVLMs through heterogeneous knowledge sources. |
MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education (2025.emnlp-demos)
Copied to clipboard
Dongsuk Jang, Ziyao Shangguan, Kyle Tegtmeyer, Anurag Gupta, Jan T Czerminski, Sophie Chheang, Arman Cohan
| Challenge: | Existing educational tools for medical residents are time-consuming and inconsistent. |
| Approach: | They propose a system that generates educational content and multiple-choice questions from clinical case reports and a pipeline that takes clinical case report input and produces targeted educational materials. |
| Outcome: | The system generates educational content and multiple-choice questions from clinical case reports and synergizes with local knowledge base to ensure it is foundationally sound and current. |