Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification

• URL: http://arxiv.org/abs/2508.15960v1
• Date: Thu, 21 Aug 2025 21:05:44 GMT
• Title: Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification
• Authors: Zhenhao Guo, Rachit Saluja, Tianyuan Yao, Quan Liu, Yuankai Huo, Benjamin Liechty, David J. Pisapia, Kenji Ikemura, Mert R. Sabuncu, Yihe Yang, Ruining Deng,
• Abstract summary: We introduce Glo-VLMs, a systematic framework designed to explore the adaptation of vision-language models to fine-grained glomerular classification.<n>Our approach leverages curated pathology images alongside clinical text prompts to facilitate joint image-text representation learning.<n>To ensure a fair comparison, we evaluate all models using standardized multi-class metrics, aiming to clarify the practical requirements and potential of large pretrained models for specialized clinical research applications.
• Score: 7.87247433522498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language models (VLMs) have shown considerable potential in digital pathology, yet their effectiveness remains limited for fine-grained, disease-specific classification tasks such as distinguishing between glomerular subtypes. The subtle morphological variations among these subtypes, combined with the difficulty of aligning visual patterns with precise clinical terminology, make automated diagnosis in renal pathology particularly challenging. In this work, we explore how large pretrained VLMs can be effectively adapted to perform fine-grained glomerular classification, even in scenarios where only a small number of labeled examples are available. In this work, we introduce Glo-VLMs, a systematic framework designed to explore the adaptation of VLMs to fine-grained glomerular classification in data-constrained settings. Our approach leverages curated pathology images alongside clinical text prompts to facilitate joint image-text representation learning for nuanced renal pathology subtypes. By assessing various VLMs architectures and adaptation strategies under a few-shot learning paradigm, we explore how both the choice of method and the amount of labeled data impact model performance in clinically relevant scenarios. To ensure a fair comparison, we evaluate all models using standardized multi-class metrics, aiming to clarify the practical requirements and potential of large pretrained models for specialized clinical research applications. As a result, fine-tuning the VLMs achieved 0.7416 accuracy, 0.9045 macro-AUC, and 0.5277 F1-score with only 8 shots per class, demonstrating that even with highly limited supervision, foundation models can be effectively adapted for fine-grained medical image classification.

Related papers

• Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data [76.89269238957593]
  Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views.<n>We propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification.
  arXiv  Detail & Related papers  (2026-02-02T13:07:52Z)
• 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)
• From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology [9.268389327736735]
  We model fine-grained glomerular subtyping as a clinically realistic few-shot problem.<n>We evaluate both pathology-specialized and general-purpose vision-language models under this setting.
  arXiv  Detail & Related papers  (2025-11-15T01:44:11Z)
• Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography [39.58317527488534]
  This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system.<n>Zero-shot classification achieved modest performance, while the fine-tuned ConvNeXt model outperformed the BioMedCLIP linear probe.<n>These findings suggest that despite the promise of multimodal learning, CNN-based models with end-to-end fine-tuning provide stronger performance for specialized medical imaging.
  arXiv  Detail & Related papers  (2025-06-16T20:14:37Z)
• In-Context Learning for Label-Efficient Cancer Image Classification in Oncology [1.741659712094955]
  In-context learning (ICL) is a pragmatic alternative to model retraining for domain-specific diagnostic tasks.<n>We evaluated the performance of four vision-language models (VLMs)-Paligemma, CLIP, ALIGN and GPT-4o.<n>ICL demonstrated competitive gains despite their smaller size, suggesting feasibility for deployment in computing constrained clinical environments.
  arXiv  Detail & Related papers  (2025-05-08T20:49:01Z)
• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification.<n>We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings.<n>A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification [41.16626194300303]
  Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks.
  arXiv  Detail & Related papers  (2023-06-16T01:46:07Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• 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)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• 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)
• Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images [4.001273534300757]
  computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. Deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Here we present CLAM - Clustering-constrained attention multiple instance learning.
  arXiv  Detail & Related papers  (2020-04-20T23:00:13Z)

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.