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

Kai Shu, Yuzhuo Jia, Ziyang Zhang, Jiechao Gao

TL;DR

FODA-PG constructs a granular graphical representation of radiological findings by separating disease-related attributes into distinct "disease-specific" and "disease-free" categories based on their clinical significance and location, enabling the model to capture the nuanced differences between normal and pathological states, mitigating the impact of data biases.

Abstract

Automatic Medical Imaging Narrative generation aims to alleviate the workload of radiologists by producing accurate clinical descriptions directly from radiological images. However, the subtle visual nuances and domain-specific terminology in medical images pose significant challenges compared to generic image captioning tasks. Existing approaches often neglect the vital distinction between normal and abnormal findings, leading to suboptimal performance. In this work, we propose FODA-PG, a novel Fine-grained Organ-Disease Adaptive Partitioning Graph framework that addresses these limitations through domain-adaptive learning. FODA-PG constructs a granular graphical representation of radiological findings by separating disease-related attributes into distinct "disease-specific" and "disease-free" categories based on their clinical significance and location. This adaptive partitioning enables our model to capture the nuanced differences between normal and pathological states, mitigating the impact of data biases. By integrating this fine-grained semantic knowledge into a powerful transformer-based architecture and providing rigorous mathematical justifications for its effectiveness, FODA-PG generates precise and clinically coherent reports with enhanced generalization capabilities. Extensive experiments on the IU-Xray and MIMIC-CXR benchmarks demonstrate the superiority of our approach over state-of-the-art methods, highlighting the importance of domain adaptation in medical report generation.

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

TL;DR

FODA-PG constructs a granular graphical representation of radiological findings by separating disease-related attributes into distinct "disease-specific" and "disease-free" categories based on their clinical significance and location, enabling the model to capture the nuanced differences between normal and pathological states, mitigating the impact of data biases.

Abstract

Automatic Medical Imaging Narrative generation aims to alleviate the workload of radiologists by producing accurate clinical descriptions directly from radiological images. However, the subtle visual nuances and domain-specific terminology in medical images pose significant challenges compared to generic image captioning tasks. Existing approaches often neglect the vital distinction between normal and abnormal findings, leading to suboptimal performance. In this work, we propose FODA-PG, a novel Fine-grained Organ-Disease Adaptive Partitioning Graph framework that addresses these limitations through domain-adaptive learning. FODA-PG constructs a granular graphical representation of radiological findings by separating disease-related attributes into distinct "disease-specific" and "disease-free" categories based on their clinical significance and location. This adaptive partitioning enables our model to capture the nuanced differences between normal and pathological states, mitigating the impact of data biases. By integrating this fine-grained semantic knowledge into a powerful transformer-based architecture and providing rigorous mathematical justifications for its effectiveness, FODA-PG generates precise and clinically coherent reports with enhanced generalization capabilities. Extensive experiments on the IU-Xray and MIMIC-CXR benchmarks demonstrate the superiority of our approach over state-of-the-art methods, highlighting the importance of domain adaptation in medical report generation.
Paper Structure (32 sections, 3 theorems, 33 equations, 4 figures)

This paper contains 32 sections, 3 theorems, 33 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Relevant Literature
  3. Visual Scene Description
  4. Medical Imaging Narrative Generation
  5. Algorithmic Framework
  6. Problem Formulation
  7. Fine-grained Organ-Disease Adaptive Partitioning Graph (FODA-PG) Construction
  8. Spectral Graph Convolution
  9. Graph Convolutional Networks and Weisfeiler-Lehman Isomorphism Test
  10. Topological Relation Enriched Image Embedding
  11. Attention as a Similarity Measure
  12. Multi-Head Attention
  13. Node-Edge Informed Narrative Construction
  14. Beam Search Decoding
  15. Reinforcement Learning for Text Generation
  16. ...and 17 more sections

Key Result

Theorem 3.1

Let $\mathcal{G}_1$ and $\mathcal{G}_2$ be two non-isomorphic graphs. If a GCN with sufficient number of layers and hidden units can distinguish $\mathcal{G}_1$ and $\mathcal{G}_2$, then the WL test can also distinguish them.

Figures (4)

  • Figure 1: Overview of FODA-PG framework, consisting of three modules: (a) Fine-grained Organ-Disease Adaptive Partitioning Graph (FODA-PG) Construction, (b) Graph-Enhanced Visual Representation, and (c) Graph-Guided Text Generation.
  • Figure 2: Evaluating Natural Language Generation and Clinical Efficacy Metrics for Multiple Techniques across Radiography Datasets.
  • Figure 3: Assessing Updated Visual Encoder Setups: (a) BioMedCLIP-pretrained ViT zhang2023biomedclip; (b) ImageNet-21K-pretrained CvT; (c) MedSAM-fine-tuned ViT for Medical Image Segmentation ma2023segment.
  • Figure 4: Node Representation and Multi-Source Integration Ablation Analysis with Revised Configurations.

Theorems & Definitions (3)

  • Theorem 3.1: WL-GCN Expressiveness xu2018powerful
  • Theorem 3.2: Expressiveness of Cross-Modal Attention tsai2019multimodal
  • Theorem 3.3: Generalization Bound for Cross-Modal Attention he2021transductive