MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention

• URL: http://arxiv.org/abs/2501.03839v1
• Date: Tue, 07 Jan 2025 14:49:12 GMT
• Title: MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention
• Authors: Aadya Arora, Vinay Namboodiri,
• Abstract summary: We propose to leverage advanced segmentation capabilities of Segment Anything Model 2 (SAM2) as a visual prompting cue to help visual encoder in the CLIP (Contrastive Language-Image Pretraining) This helps the model to focus on highly discriminative regions, without getting distracted from visually similar background features. We evaluate our method on diverse medical datasets including X-rays, CT scans, and MRI images, and report an accuracy of (71%, 81%, 86%, 58%) from the proposed approach.
• Score: 1.2277343096128712
• License:
• Abstract: With the popularity of foundational models, parameter efficient fine tuning has become the defacto approach to leverage pretrained models to perform downstream tasks. Taking inspiration from recent advances in large language models, Visual Prompt Tuning, and similar techniques, learn an additional prompt to efficiently finetune a pretrained vision foundational model. However, we observe that such prompting is insufficient for fine-grained visual classification tasks such as medical image classification, where there is large inter-class variance, and small intra-class variance. Hence, in this paper we propose to leverage advanced segmentation capabilities of Segment Anything Model 2 (SAM2) as a visual prompting cue to help visual encoder in the CLIP (Contrastive Language-Image Pretraining) by guiding the attention in CLIP visual encoder to relevant regions in the image. This helps the model to focus on highly discriminative regions, without getting distracted from visually similar background features, an essential requirement in a fewshot, finegrained classification setting. We evaluate our method on diverse medical datasets including X-rays, CT scans, and MRI images, and report an accuracy of (71%, 81%, 86%, 58%) from the proposed approach on (COVID, lung-disease, brain-tumor, breast-cancer) datasets against (66%, 70%, 68%, 29%) from a pretrained CLIP model after fewshot training. The proposed approach also allows to obtain interpretable explanation for the classification performance through the localization obtained using segmentation.

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