PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis

• URL: http://arxiv.org/abs/2506.10669v2
• Date: Fri, 13 Jun 2025 08:57:35 GMT
• Title: PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis
• Authors: Marzieh Oghbaie, Teresa Araújo, Hrvoje Bogunović,
• Abstract summary: We propose PiPViT (Patch-based Visual Interpretable Prototypes), an inherently interpretable prototypical model for image recognition.<n>PiPViT captures long-range dependencies among patches to learn robust, human-interpretable prototypes.<n>We evaluate PiPViT on retinal OCT image classification across four datasets.
• Score: 0.13406576408866772
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background and Objective: Prototype-based methods improve interpretability by learning fine-grained part-prototypes; however, their visualization in the input pixel space is not always consistent with human-understandable biomarkers. In addition, well-known prototype-based approaches typically learn extremely granular prototypes that are less interpretable in medical imaging, where both the presence and extent of biomarkers and lesions are critical. Methods: To address these challenges, we propose PiPViT (Patch-based Visual Interpretable Prototypes), an inherently interpretable prototypical model for image recognition. Leveraging a vision transformer (ViT), PiPViT captures long-range dependencies among patches to learn robust, human-interpretable prototypes that approximate lesion extent only using image-level labels. Additionally, PiPViT benefits from contrastive learning and multi-resolution input processing, which enables effective localization of biomarkers across scales. Results: We evaluated PiPViT on retinal OCT image classification across four datasets, where it achieved competitive quantitative performance compared to state-of-the-art methods while delivering more meaningful explanations. Moreover, quantitative evaluation on a hold-out test set confirms that the learned prototypes are semantically and clinically relevant. We believe PiPViT can transparently explain its decisions and assist clinicians in understanding diagnostic outcomes. Github page: https://github.com/marziehoghbaie/PiPViT

Related papers

• CountXplain: Interpretable Cell Counting with Prototype-Based Density Map Estimation [1.609940380983903]
  We propose a novel prototype-based method for interpretable cell counting via density map estimation.<n>Our approach integrates a prototype layer into the density estimation network, enabling the model to learn representative visual patterns for both cells and background artifacts.
  arXiv  Detail & Related papers  (2025-11-24T20:47:44Z)
• ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning [0.21079694661943607]
  ProtoECGNet is a prototype deep learning model for interpretable, multilabel ECG classification.<n>We evaluate ProtoECGNet on all 71 diagnostic labels from the PTB-XL dataset.<n>ProtoECGNet shows that prototype learning can be effectively scaled to complex, multi-label time-series classification.
  arXiv  Detail & Related papers  (2025-04-11T17:23:37Z)
• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors.<n>Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images.<n>Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• 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)
• Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation [15.303610605543746]
  We present a novel Cross- and Intra-image Prototypical Learning framework, for accurate multi-label disease diagnosis and interpretation from medical images.<n>We propose a new two-level alignment-based regularisation strategy that effectively leverages consistent intra-image information to enhance interpretation robustness and predictive performance.
  arXiv  Detail & Related papers  (2024-11-07T10:46:01Z)
• 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)
• ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification [16.415582577355536]
  Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve performance and capture label dependencies. We propose Probabilistic Multi-label Contrastive Learning (ProbMCL), a novel framework to address these challenges.
  arXiv  Detail & Related papers  (2024-01-02T22:15:20Z)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology [5.164102666113966]
  We conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks. Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images.
  arXiv  Detail & Related papers  (2022-03-01T16:14:41Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.