From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2504.11368v1
• Date: Tue, 15 Apr 2025 16:32:15 GMT
• Title: From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation
• Authors: Jingkun Chen, Haoran Duan, Xiao Zhang, Boyan Gao, Tao Tan, Vicente Grau, Jungong Han,
• Abstract summary: Vision-language models (VLMs) provide semantic context through textual descriptions but lack explanation precision required.<n>We propose a teacher-student framework that integrates both gaze and language supervision, leveraging their complementary strengths.<n>Our method achieves Dice scores of 80.78%, 80.53%, and 84.22%, respectively, improving 3-5% over gaze baselines without increasing the annotation burden.
• Score: 46.99748372216857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation remains challenging due to the high cost of pixel-level annotations for training. In the context of weak supervision, clinician gaze data captures regions of diagnostic interest; however, its sparsity limits its use for segmentation. In contrast, vision-language models (VLMs) provide semantic context through textual descriptions but lack the explanation precision required. Recognizing that neither source alone suffices, we propose a teacher-student framework that integrates both gaze and language supervision, leveraging their complementary strengths. Our key insight is that gaze data indicates where clinicians focus during diagnosis, while VLMs explain why those regions are significant. To implement this, the teacher model first learns from gaze points enhanced by VLM-generated descriptions of lesion morphology, establishing a foundation for guiding the student model. The teacher then directs the student through three strategies: (1) Multi-scale feature alignment to fuse visual cues with textual semantics; (2) Confidence-weighted consistency constraints to focus on reliable predictions; (3) Adaptive masking to limit error propagation in uncertain areas. Experiments on the Kvasir-SEG, NCI-ISBI, and ISIC datasets show that our method achieves Dice scores of 80.78%, 80.53%, and 84.22%, respectively-improving 3-5% over gaze baselines without increasing the annotation burden. By preserving correlations among predictions, gaze data, and lesion descriptions, our framework also maintains clinical interpretability. This work illustrates how integrating human visual attention with AI-generated semantic context can effectively overcome the limitations of individual weak supervision signals, thereby advancing the development of deployable, annotation-efficient medical AI systems. Code is available at: https://github.com/jingkunchen/FGI.git.

Related papers

• Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis [44.38638601819933]
  Current staging models for Diabetic Retinopathy (DR) are hardly interpretable.<n>We present a novel method that integrates graph representation learning with vision-language models (VLMs) to deliver explainable DR diagnosis.
  arXiv  Detail & Related papers  (2025-03-12T20:19:07Z)
• FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification [4.148491257542209]
  Few-shot learning presents a critical solution for cancer diagnosis in computational pathology. A key challenge in this paradigm stems from the inherent disparity between the limited training set of whole slide images (WSIs) and the enormous number of contained patches. We introduce the knowledge-enhanced adaptive visual compression framework, dubbed FOCUS, to enable a focused analysis of diagnostically relevant regions.
  arXiv  Detail & Related papers  (2024-11-22T05:36:38Z)
• Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation [55.325956390997]
  This paper proposes an affinity-graph-guided semi-supervised contrastive learning framework (Semi-AGCL) for medical image segmentation. The framework first designs an average-patch-entropy-driven inter-patch sampling method, which can provide a robust initial feature space. With merely 10% of the complete annotation set, our model approaches the accuracy of the fully annotated baseline, manifesting a marginal deviation of only 2.52%.
  arXiv  Detail & Related papers  (2024-10-14T10:44:47Z)
• ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features [54.37042005469384]
  We announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining. We propose ViKL, a framework that synergizes Visual, Knowledge, and Linguistic features.
  arXiv  Detail & Related papers  (2024-09-24T05:01:23Z)
• Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning [65.54680361074882]
  Eye-gaze Guided Multi-modal Alignment (EGMA) framework harnesses eye-gaze data for better alignment of medical visual and textual features. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets.
  arXiv  Detail & Related papers  (2024-03-19T03:59:14Z)
• Anatomical Structure-Guided Medical Vision-Language Pre-training [21.68719061251635]
  We propose an Anatomical Structure-Guided (ASG) framework for learning medical visual representations. For anatomical region, we design an automatic anatomical region-sentence alignment paradigm in collaboration with radiologists. For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample.
  arXiv  Detail & Related papers  (2024-03-14T11:29:47Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Latent Graph Representations for Critical View of Safety Assessment [2.9724186623561435]
  We propose a method for CVS prediction wherein we first represent a surgical image using a disentangled latent scene graph, then process this representation using a graph neural network. Our graph representations explicitly encode semantic information to improve anatomy-driven reasoning, as well as visual features to retain differentiability and thereby provide robustness to semantic errors. We show that our method not only outperforms several baseline methods when trained with bounding box annotations, but also scales effectively when trained with segmentation masks, maintaining state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-12-08T09:21:09Z)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [47.999621481852266]
  We propose an interpretable graph learning-based model which interprets the clinical relevance of the input features towards the task. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task.
  arXiv  Detail & Related papers  (2021-03-29T13:04:02Z)

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.