Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning

• URL: http://arxiv.org/abs/2505.15687v1
• Date: Wed, 21 May 2025 16:03:03 GMT
• Title: Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning
• Authors: Zhe Xu, Cheng Jin, Yihui Wang, Ziyi Liu, Hao Chen,
• Abstract summary: Multimodal pathological image understanding has garnered widespread interest due to its potential to improve diagnostic accuracy.<n>Existing methods exhibit limited reasoning capabilities, which hamper their ability to handle complex diagnostic scenarios.<n>We introduce a novel bilateral reinforcement learning framework comprising two synergistic branches.
• Score: 25.707757721296627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal pathological image understanding has garnered widespread interest due to its potential to improve diagnostic accuracy and enable personalized treatment through integrated visual and textual data. However, existing methods exhibit limited reasoning capabilities, which hamper their ability to handle complex diagnostic scenarios. Additionally, the enormous size of pathological images leads to severe computational burdens, further restricting their practical deployment. To address these limitations, we introduce a novel bilateral reinforcement learning framework comprising two synergistic branches. One reinforcement branch enhances the reasoning capability by enabling the model to learn task-specific decision processes, i.e., pathology rationales, directly from labels without explicit reasoning supervision. While the other branch dynamically allocates a tailored number of tokens to different images based on both their visual content and task context, thereby optimizing computational efficiency. We apply our method to various pathological tasks such as visual question answering, cancer subtyping, and lesion detection. Extensive experiments show an average +41.7 absolute performance improvement with 70.3% lower inference costs over the base models, achieving both reasoning accuracy and computational efficiency.

Related papers

• PathReasoner-R1: Instilling Structured Reasoning into Pathology Vision-Language Model via Knowledge-Guided Policy Optimization [6.821738567680833]
  We construct PathReasoner, the first large-scale dataset of whole-slide image (WSI) reasoning.<n>PathReasoner-R1 synergizes supervised fine-tuning with reasoning-oriented reinforcement learning to instill structured chain-of-thought capabilities.<n>Experiments demonstrate that PathReasoner-R1 achieves state-of-the-art performance on both PathReasoner and public benchmarks across various image scales.
  arXiv  Detail & Related papers  (2026-01-29T12:21:16Z)
• Anatomy-R1: Enhancing Anatomy Reasoning in Multimodal Large Language Models via Anatomical Similarity Curriculum and Group Diversity Augmentation [52.7583577508452]
  Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning.<n>Their potential in medical imaging remains underexplored, especially in clinical anatomical surgical images.<n>These challenges limit the effectiveness of conventionalSupervised Fine-Tuning strategies.
  arXiv  Detail & Related papers  (2025-12-22T16:06:36Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole Slide Image Diagnosis Behavior [6.583135094946921]
  We introduce a framework designed to address this challenge through three key breakthroughs.<n>First, the AI Session Recorder seamlessly integrates with standard whole-slide image viewers.<n>Second, a lightweight human-in-the-loop review turns AI-drafted rationales for behavioral commands into the Pathology-CoT dataset.<n>Third, our framework makes agentic pathology practical and establishes a path to human-aligned, upgradeable clinical AI.
  arXiv  Detail & Related papers  (2025-10-06T08:44:04Z)
• RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis [56.373297358647655]
  Retrieval-Augmented Diagnosis (RAD) is a novel framework that injects external knowledge into multimodal models directly on downstream tasks.<n>RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guideline-enhanced contrastive loss transformer, and a dual decoder.
  arXiv  Detail & Related papers  (2025-09-24T10:36:14Z)
• Evidence-based diagnostic reasoning with multi-agent copilot for human pathology [7.976907866539546]
  Current multimodal large language models (MLLMs) in computational pathology face limitations.<n>We introduce PathChat+, a new MLLM specifically designed for human pathology, trained on over 1 million diverse, pathology-specific instruction samples.<n>We also present SlideSeek, a reasoning-enabled multi-agent AI system leveraging PathChat+ to autonomously evaluate gigapixel whole-slide images.
  arXiv  Detail & Related papers  (2025-06-26T03:02:16Z)
• Pre-trained Models Succeed in Medical Imaging with Representation Similarity Degradation [6.545152478351316]
  The study establishes a rigorous problem definition centered on quantifying and analyzing representation similarity trajectories.<n>Our empirical findings reveal the potential existence of high-performance models that preserve both task accuracy and representation similarity to their pre-trained origins.
  arXiv  Detail & Related papers  (2025-03-11T01:37:54Z)
• A Deep Learning Approach for Augmenting Perceptional Understanding of Histopathology Images [0.1813006808606333]
  This Paper Presents A Novel Approach To Enhancing The Analysis Of Histopathology Images.<n>A Mult-modal-Model That Combines Vision Transformers (Vit) With Gpt-2 For Image Captioning.
  arXiv  Detail & Related papers  (2025-03-10T03:50:25Z)
• Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis [37.11302829771659]
  Large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy in pathology image analysis.<n>We propose two innovative strategies: the mixed task-guided feature enhancement, and the prompt-guided detail feature completion.<n>We trained the pathology-specialized LVLM, OmniPath, which significantly outperforms existing methods in diagnostic accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-12-12T18:07:23Z)
• Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation [3.1002416427168304]
  This thesis focuses on retinal blood vessel segmentation tasks. It provides an extensive literature review of deep learning-based medical image segmentation approaches. It proposes a novel efficient, simple multiview learning framework.
  arXiv  Detail & Related papers  (2023-11-02T06:31:08Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Unified Representation Learning for Efficient Medical Image Analysis [0.623075162128532]
  We propose a multi-task training approach for medical image analysis using a unified modality-specific feature representation (UMS-Rep) Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance.
  arXiv  Detail & Related papers  (2020-06-19T16:52:16Z)

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.