WoLF: Wide-scope Large Language Model Framework for CXR Understanding

• URL: http://arxiv.org/abs/2403.15456v3
• Date: Fri, 29 Mar 2024 04:38:51 GMT
• Title: WoLF: Wide-scope Large Language Model Framework for CXR Understanding
• Authors: Seil Kang, Donghyun Kim, Junhyeok Kim, Hyo Kyung Lee, Seong Jae Hwang,
• Abstract summary: We introduce Wide-scope Large Language Model Framework for Chest X-ray understanding. We capture multi-faceted records of patients, which are utilized for accurate diagnoses in real-world clinical scenarios.
• Score: 8.265578494822087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Significant methodological strides have been made toward Chest X-ray (CXR) understanding via modern vision-language models (VLMs), demonstrating impressive Visual Question Answering (VQA) and CXR report generation abilities. However, existing CXR understanding frameworks still possess several procedural caveats. (1) Previous methods solely use CXR reports, which are insufficient for comprehensive Visual Question Answering (VQA), especially when additional health-related data like medication history and prior diagnoses are needed. (2) Previous methods use raw CXR reports, which are often arbitrarily structured. While modern language models can understand various text formats, restructuring reports for clearer, organized anatomy-based information could enhance their usefulness. (3) Current evaluation methods for CXR-VQA primarily emphasize linguistic correctness, lacking the capability to offer nuanced assessments of the generated answers. In this work, to address the aforementioned caveats, we introduce WoLF, a Wide-scope Large Language Model Framework for CXR understanding. To resolve (1), we capture multi-faceted records of patients, which are utilized for accurate diagnoses in real-world clinical scenarios. Specifically, we adopt the Electronic Health Records (EHR) to generate instruction-following data suited for CXR understanding. Regarding (2), we enhance report generation performance by decoupling knowledge in CXR reports based on anatomical structure even within the attention step via masked attention. To address (3), we introduce an AI-evaluation protocol optimized for assessing the capabilities of LLM. Through extensive experimental validations, WoLF demonstrates superior performance over other models on MIMIC-CXR in the AI-evaluation arena about VQA (up to +9.47%p mean score) and by metrics about report generation (+7.3%p BLEU-1).

Related papers

• CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting [0.0]
  We evaluate the publicly available, state of the art, foundational vision-language models for chest X-ray interpretation. We find that vision-language models often hallucinate with confident language, which slows down clinical interpretation. We develop an agent-based vision-language approach for report generation using CheXagent's linear probes and BioViL-T's phrase grounding tools.
  arXiv  Detail & Related papers  (2024-07-11T18:39:19Z)
• CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation [21.31741755127183]
  Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice. Recent advances in the development of vision-language foundation models (FMs) give rise to the possibility of performing automated CXR interpretation.
  arXiv  Detail & Related papers  (2024-01-22T18:51:07Z)
• Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation [7.586632627817609]
  Radiologists face high burnout rates, partly due to the increasing volume of Chest X-rays (CXRs) requiring interpretation and reporting. Our proposed CXR report generator integrates elements of the workflow and introduces a novel reward for reinforcement learning. Results from our study demonstrate that the proposed model generates reports that are more aligned with radiologists' reports than state-of-the-art models.
  arXiv  Detail & Related papers  (2023-07-19T05:41:14Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors [1.1110995501996481]
  We propose two methods to remove references to priors in radiology reports. A GPT-3-based few-shot approach to rewrite medical reports without references to priors; and a BioBERT-based token classification approach to directly remove words referring to priors. We find that our re-trained model--which we call CXR-ReDonE--outperforms previous report generation methods on clinical metrics, achieving an average BERTScore of 0.2351 (2.57% absolute improvement)
  arXiv  Detail & Related papers  (2022-09-27T00:44:41Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment [27.111857943935725]
  We present an automatic, multi-modal approach for report generation from chest x-ray. Our approach features two distinct modules: (i) Learned knowledge base and (ii) Multi-modal alignment. With the aid of both modules, our approach clearly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-30T10:43:56Z)
• Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation [107.3538598876467]
  We propose an Auxiliary Signal-Guided Knowledge-Decoder (ASGK) to mimic radiologists' working patterns. ASGK integrates internal visual feature fusion and external medical linguistic information to guide medical knowledge transfer and learning.
  arXiv  Detail & Related papers  (2020-06-06T01:00:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.