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

arXiv: https://arxiv.org/abs/2604.23977 · HTML: https://mlsanity.com/paper/2604.23977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 62 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling](https://mlsanity.com/paper/2111.03930/) (2021)
- [Prompt-aligned Gradient for Prompt Tuning](https://mlsanity.com/paper/2205.14865/) (2022)
- [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://mlsanity.com/paper/2303.13283/) (2023)
- [Self-regulating Prompts: Foundational Model Adaptation without Forgetting](https://mlsanity.com/paper/2307.06948/) (2023)
- [Prior Guided Feature Enrichment Network for Few-Shot Segmentation](https://mlsanity.com/paper/2008.01449/) (2020)
- [Prompt Distribution Learning](https://mlsanity.com/paper/2205.03340/) (2022)
- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [Low-Rank Few-Shot Adaptation of Vision-Language Models](https://mlsanity.com/paper/2405.18541/) (2024)
- [A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models](https://mlsanity.com/paper/2312.12730/) (2023)
- [LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP](https://mlsanity.com/paper/2404.02285/) (2024)
- [A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation](https://mlsanity.com/paper/2402.04087/) (2024)
- [Bayesian Prompt Learning for Image-Language Model Generalization](https://mlsanity.com/paper/2210.02390/) (2022)
- [COVID-19 Infection Localization and Severity Grading from Chest X-ray Images](https://mlsanity.com/paper/2103.07985/) (2021)
- [PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask](https://mlsanity.com/paper/2109.13788/) (2021)
- [BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models](https://mlsanity.com/paper/2411.15232/) (2024)
- [Domain-Controlled Prompt Learning](https://mlsanity.com/paper/2310.07730/) (2023)
- [XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization](https://mlsanity.com/paper/2403.09410/) (2024)
- [ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models](https://mlsanity.com/paper/2501.11175/) (2025)
- [Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval](https://mlsanity.com/paper/2302.06081/) (2023)
- [Aligning Medical Images with General Knowledge from Large Language Models](https://mlsanity.com/paper/2409.00341/) (2024)
- [Semantic-Consistent Bidirectional Contrastive Hashing for Noisy Multi-Label Cross-Modal Retrieval](https://mlsanity.com/paper/2511.07780/) (2025)

## Abstract (arXiv)

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.