Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation

• URL: http://arxiv.org/abs/2412.13599v1
• Date: Wed, 18 Dec 2024 08:31:26 GMT
• Title: Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation
• Authors: Jinghan Sun, Dong Wei, Zhe Xu, Donghuan Lu, Hong Liu, Hong Wang, Sotirios A. Tsaftaris, Steven McDonagh, Yefeng Zheng, Liansheng Wang,
• Abstract summary: Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. Existing methods often focused on either task separately, ignoring their correlation. This work proposes a co-evolutionary abnormality detection and report generation framework.
• Score: 44.41011570487469
• License:
• Abstract: Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the findings in a detailed report for further diagnosis and treatment. Existing methods often focused on either task separately, ignoring their correlation. This work proposes a co-evolutionary abnormality detection and report generation (CoE-DG) framework. The framework utilizes both fully labeled (with bounding box annotations and clinical reports) and weakly labeled (with reports only) data to achieve mutual promotion between the abnormality detection and report generation tasks. Specifically, we introduce a bi-directional information interaction strategy with generator-guided information propagation (GIP) and detector-guided information propagation (DIP). For semi-supervised abnormality detection, GIP takes the informative feature extracted by the generator as an auxiliary input to the detector and uses the generator's prediction to refine the detector's pseudo labels. We further propose an intra-image-modal self-adaptive non-maximum suppression module (SA-NMS). This module dynamically rectifies pseudo detection labels generated by the teacher detection model with high-confidence predictions by the student.Inversely, for report generation, DIP takes the abnormalities' categories and locations predicted by the detector as input and guidance for the generator to improve the generated reports.

Related papers

• Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation [10.46031380503486]
  We introduce a novel method, textbfStructural textbfEntities extraction and patient indications textbfIncorporation (SEI) for chest X-ray report generation. We employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports. We propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications.
  arXiv  Detail & Related papers  (2024-05-23T01:29:47Z)
• You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray [33.272751884183556]
  Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods.
  arXiv  Detail & Related papers  (2023-07-18T12:18:21Z)
• 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)
• Cross-Modal Causal Intervention for Medical Report Generation [109.83549148448469]
  Medical report generation (MRG) is essential for computer-aided diagnosis and medication guidance. Due to the spurious correlations within image-text data induced by visual and linguistic biases, it is challenging to generate accurate reports reliably describing lesion areas. We propose a novel Visual-Linguistic Causal Intervention (VLCI) framework for MRG, which consists of a visual deconfounding module (VDM) and a linguistic deconfounding module (LDM)
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation [7.118069629513661]
  We introduce a novel fined-grained knowledge graph structure called an attributed abnormality graph (ATAG) The ATAG consists of interconnected abnormality nodes and attribute nodes, allowing it to better capture the abnormality details. We show that the proposed ATAG-based deep model outperforms the SOTA methods by a large margin and can improve the clinical accuracy of the generated reports.
  arXiv  Detail & Related papers  (2022-07-04T05:32:00Z)
• Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training [23.506879497561712]
  We employ a contrastive global-local dual-encoder architecture to learn concepts directly from unstructured medical reports. We evaluate our approach on the large-scale chest X-Ray datasets MIMIC-CXR, CheXpert, and ChestX-Ray14 for disease classification.
  arXiv  Detail & Related papers  (2022-05-14T21:44:05Z)
• Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation [55.00308939833555]
  The PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD) PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias. PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
  arXiv  Detail & Related papers  (2021-06-13T11:10:02Z)
• Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays [6.686095511538683]
  This work focuses on reporting abnormal findings on radiology images. We propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules. We demonstrate that our method is able to retrieve abnormal findings and outperforms existing generation models on both clinical correctness and text generation metrics.
  arXiv  Detail & Related papers  (2020-10-06T04:18:18Z)
• Chest X-ray Report Generation through Fine-Grained Label Learning [46.352966049776875]
  We present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings.
  arXiv  Detail & Related papers  (2020-07-27T19:50:56Z)
• 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.