Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification

• URL: http://arxiv.org/abs/2604.23977v1
• Date: Mon, 27 Apr 2026 02:41:27 GMT
• Title: Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification
• Abstract summary: Multi-View Synergistic Learning (MVSL) is a unified framework that addresses adaptation paradigms, representation granularity, and disease semantic relationships.<n>MVSL decouples the adaptation of visual and textual encoders to respect their distinct representational characteristics.<n>It further introduces multi-granularity contrastive learning to explicitly model both global image semantics and localized lesion-level evidence.<n>MVSL consistently outperforms state-of-the-art methods in few-shot and zero-shot classification settings.
• Score: 59.24009931000134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate biomedical image classification under low-resource conditions remains challenging due to limited annotations, subtle inter-class visual differences, and complex disease semantics. While vision--language models offer a promising foundation for mitigating data scarcity, their effective adaptation in biomedical settings is constrained by the need for parameter-efficient tuning alongside fine-grained and semantically consistent representation learning. In this work, we propose Multi-View Synergistic Learning (MVSL), a unified framework that addresses these challenges by jointly considering adaptation paradigms, representation granularity, and disease semantic relationships. MVSL decouples the adaptation of visual and textual encoders to respect their distinct representational characteristics, enabling more stable and effective parameter-efficient fine-tuning. It further introduces multi-granularity contrastive learning to explicitly model both global image semantics and localized lesion-level evidence, improving fine-grained discrimination for visually similar disease categories. In addition, MVSL preserves disease-level semantic structure by incorporating structured supervision derived from large language models, which constrains textual representations at the class level and indirectly regularizes visual embeddings through cross-modal alignment. Together, these components enable more stable cross-modal alignment and improved discrimination under limited supervision. Extensive experiments on $11$ public biomedical datasets spanning $9$ imaging modalities and $10$ anatomical regions demonstrate that MVSL consistently outperforms state-of-the-art methods in few-shot and zero-shot classification settings.

Related papers

• STAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models [36.10815998300494]
  We propose a novel framework termed Semantic Text-Anchored Incremental Learning (STAIL) for sequential clinical tasks.<n>By incorporating a minimal set of image anchors and extensive textual descriptions, the SCB enables dense semantic reconstruction of old tasks at a minimal storage cost.<n>We show that STAIL acts as a highly effective plug-and-play module, achieving average gains of 2.24% in AAA-AUC for sustained performance and 3.55% in BWT-AUC for reduced forgetting.
  arXiv  Detail & Related papers  (2026-08-06T09:42:13Z)
• EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment [15.371433625713232]
  EndoVLM is a novel vision-language FM pre-trained on over 348K endoscopic examinations.<n>An Anatomy-Guided Sparse Pooling mechanism utilizes textual descriptions as queries to drive sparse attention.<n>A Progressive Semantic-Aware Alignment strategy models clinical taxonomy (anatomy and pathological status) via structured soft targets.<n>A Semantic-Concentrated Masked Autoencoder is applied exclusively to these semantic-rich frames, integrating low-level visual precision with robust high-level semantic representation.
  arXiv  Detail & Related papers  (2026-08-05T05:56:41Z)
• MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models [83.50100003741628]
  Vision and language models (VLMs) hold immense promise to transform biomedical imaging.<n>We introduce the Massive Multimodal Biomedical Understanding (MMBU) benchmark.<n>It is the largest vision and language benchmark to date, covering 35 submodalities with rich structured metadata.
  arXiv  Detail & Related papers  (2026-06-04T20:24:47Z)
• 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)
• Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification [7.87247433522498]
  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.
  arXiv  Detail & Related papers  (2025-08-21T21:05:44Z)
• A Vision-Language Foundation Model for Leaf Disease Identification [0.0]
  Leaf disease identification plays a pivotal role in smart agriculture.<n>Many existing studies still struggle to integrate image and textual modalities to compensate for each other's limitations.<n>We propose SCOLD, a context-aware vision-language foundation model to address these challenges.
  arXiv  Detail & Related papers  (2025-05-11T15:30:06Z)
• 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)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [57.044719143401664]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis [6.4136876268620115]
  Self-supervised learning (SSL) has emerged as a promising technique for analyzing medical modalities such as X-rays.<n>We propose OTCXR, a novel SSL framework that leverages optimal transport (OT) to learn dense semantic invariance.<n>We validate OTCXR's efficacy through comprehensive experiments on three publicly available chest X-ray datasets.
  arXiv  Detail & Related papers  (2024-04-18T02:59:48Z)
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing [53.89917396428747]
  Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. We explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
  arXiv  Detail & Related papers  (2023-01-11T16:35:33Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)

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.