Challenge: X-ray and CT are the gold standard for COVID-19 diagnosis and treatment . however, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists.
Approach: They propose to use X-ray and CT to generate medical reports automatically . they evaluate DeltaNet on a COVID-19 dataset, where it outperforms state-of-the-art approaches .
Outcome: The proposed system outperforms state-of-the-art methods on a COVID-19 dataset.

Similar Papers

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.
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)

Copied to clipboard

Challenge: Radiology report generation aims at generating descriptive text from radiology images automatically.
Approach: They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically.
Outcome: The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks.
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.
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays (2020.findings-emnlp)

Copied to clipboard

Challenge: Existing work on report generation often trains encoder-decoder networks to generate complete reports, but such models are affected by data bias and face common issues inherent in text generation models.
Approach: They propose a method to identify abnormal findings from radiology images and group them with unsupervised clustering and minimal rules.
Outcome: The proposed method outperforms existing generation models on correctness and text generation metrics.
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

Copied to clipboard

Challenge: under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach: They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome: The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.
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.
A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning (2024.findings-acl)

Copied to clipboard

Challenge: Diagnostic Captioning (DC) systems receive one or more medical images of a patient, such as X-Rays or Magnetic Resonance Images (MRIs).
Approach: They propose a data-driven guided decoding method that incorporates medical information into the beam search of the diagnostic text generation process.
Outcome: The proposed method improves on two medical datasets and can be used in few- and zero-shot learning scenarios.
Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations (2022.aacl-main)

Copied to clipboard

Challenge: Existing approaches to generate text radiology reports are prone to errors and poor clinical accuracy.
Approach: They propose a two-step pipeline that subdivides the problem into factual triple extraction followed by free-text report generation.
Outcome: The proposed pipeline shows that the generated reports exhibit realistic style but lack clinical accuracy.
Contrastive Attention for Automatic Chest X-ray Report Generation (2021.findings-acl)

Copied to clipboard

Challenge: Recent studies show that learning-based models fail to accurately capture and describe abnormal regions due to data bias.
Approach: They propose a model that compares the current input image with normal images to capture abnormal regions by contrasting the input image and normal images.
Outcome: The proposed model can be easily incorporated into existing models to boost their performance under most metrics.
Enhancing Image-to-Text Generation in Radiology Reports through Cross-modal Multi-Task Learning (2024.lrec-main)

Copied to clipboard

Challenge: Image-to-text generation relies on independent models for image understanding and natural language generation, which often exhibit a semantic gap between visual and textual information.
Approach: They propose a multi-task learning framework to leverage both visual and non-imaging data for generating radiology reports.
Outcome: The proposed framework improves performance over single-task baselines across language generation metrics and mitigates overfitting in auxiliary tasks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations