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PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding

Dawei Dai, Yuanhui Zhang, Long Xu, Qianlan Yang, Xiaojing Shen, Shuyin Xia, Guoyin Wang

TL;DR

PA-LLaVA advances pathology image understanding by tailoring a vision-language assistant to the domain. It introduces a pathology-tuned PLIP visual encoder, a scale-invariant connector, and a three-stage training pipeline that leverages publicly available pathology image-text data and VQA datasets. The approach achieves state-of-the-art performance on supervised and zero-shot pathology VQA tasks and demonstrates the value of domain-specific data alignment for reliable multimodal reasoning. By releasing datasets, model components, and training code, this work aims to accelerate research in computational pathology.

Abstract

The previous advancements in pathology image understanding primarily involved developing models tailored to specific tasks. Recent studies has demonstrated that the large vision-language model can enhance the performance of various downstream tasks in medical image understanding. In this study, we developed a domain-specific large language-vision assistant (PA-LLaVA) for pathology image understanding. Specifically, (1) we first construct a human pathology image-text dataset by cleaning the public medical image-text data for domain-specific alignment; (2) Using the proposed image-text data, we first train a pathology language-image pretraining (PLIP) model as the specialized visual encoder for pathology image, and then we developed scale-invariant connector to avoid the information loss caused by image scaling; (3) We adopt two-stage learning to train PA-LLaVA, first stage for domain alignment, and second stage for end to end visual question \& answering (VQA) task. In experiments, we evaluate our PA-LLaVA on both supervised and zero-shot VQA datasets, our model achieved the best overall performance among multimodal models of similar scale. The ablation experiments also confirmed the effectiveness of our design. We posit that our PA-LLaVA model and the datasets presented in this work can promote research in field of computational pathology. All codes are available at: https://github.com/ddw2AIGROUP2CQUPT/PA-LLaVA}{https://github.com/ddw2AIGROUP2CQUPT/PA-LLaVA

PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding

TL;DR

PA-LLaVA advances pathology image understanding by tailoring a vision-language assistant to the domain. It introduces a pathology-tuned PLIP visual encoder, a scale-invariant connector, and a three-stage training pipeline that leverages publicly available pathology image-text data and VQA datasets. The approach achieves state-of-the-art performance on supervised and zero-shot pathology VQA tasks and demonstrates the value of domain-specific data alignment for reliable multimodal reasoning. By releasing datasets, model components, and training code, this work aims to accelerate research in computational pathology.

Abstract

The previous advancements in pathology image understanding primarily involved developing models tailored to specific tasks. Recent studies has demonstrated that the large vision-language model can enhance the performance of various downstream tasks in medical image understanding. In this study, we developed a domain-specific large language-vision assistant (PA-LLaVA) for pathology image understanding. Specifically, (1) we first construct a human pathology image-text dataset by cleaning the public medical image-text data for domain-specific alignment; (2) Using the proposed image-text data, we first train a pathology language-image pretraining (PLIP) model as the specialized visual encoder for pathology image, and then we developed scale-invariant connector to avoid the information loss caused by image scaling; (3) We adopt two-stage learning to train PA-LLaVA, first stage for domain alignment, and second stage for end to end visual question \& answering (VQA) task. In experiments, we evaluate our PA-LLaVA on both supervised and zero-shot VQA datasets, our model achieved the best overall performance among multimodal models of similar scale. The ablation experiments also confirmed the effectiveness of our design. We posit that our PA-LLaVA model and the datasets presented in this work can promote research in field of computational pathology. All codes are available at: https://github.com/ddw2AIGROUP2CQUPT/PA-LLaVA}{https://github.com/ddw2AIGROUP2CQUPT/PA-LLaVA
Paper Structure (18 sections, 4 figures, 4 tables)

This paper contains 18 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Medical Image-Text Datasets.
  4. LLaVA for Medical Field.
  5. Methodology
  6. Data Construction
  7. Pathology Image-Caption Data
  8. VAQ Data for Stage 3
  9. VQA Data for Zero-Shot Test
  10. Architecture of PA-LLaVA
  11. Three-Stages Learning for PA-LLaVA
  12. Experiments
  13. Evaluation Metrics
  14. Implementation details
  15. Comparisons with SOTA
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Demonstration of our PA-LLaVA. It is capable of answering various questions based on the pathology image. In this study, description generation is only an intermediate stage that requires high-quality pathological image-text data for fune-tuning to improve its quality.
  • Figure 2: Overview of our PA-LLaVA. (a) Piplines of constructing the dataset of human pathology image-text pairs; (b) Architecture of our PA-LLaVA model; (c) Three-stages learning for PA-LLaVA.
  • Figure 3: Frequencies of noun words in our VQA training data.
  • Figure 4: Illustrations of our instruction-following data.