CLIP-IT: CLIP-based Pairing for Histology Images Classification

• URL: http://arxiv.org/abs/2504.16181v1
• Date: Tue, 22 Apr 2025 18:14:43 GMT
• Title: CLIP-IT: CLIP-based Pairing for Histology Images Classification
• Authors: Banafsheh Karimian, Giulia Avanzato, Soufian Belharbi, Luke McCaffrey, Mohammadhadi Shateri, Eric Granger,
• Abstract summary: We introduce CLIP-IT to train a vision backbone model to classify histology images by pairing them with privileged textual information from an external source.<n>At first, the modality pairing step relies on a CLIP-based model to match histology images with semantically relevant textual report data from external sources, creating an augmented multimodal dataset.<n>A parameter-efficient fine-tuning method is used to efficiently address the misalignment between the main (image) and paired (text) modalities.
• Score: 6.855390956571216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal learning has shown significant promise for improving medical image analysis by integrating information from complementary data sources. This is widely employed for training vision-language models (VLMs) for cancer detection based on histology images and text reports. However, one of the main limitations in training these VLMs is the requirement for large paired datasets, raising concerns over privacy, and data collection, annotation, and maintenance costs. To address this challenge, we introduce CLIP-IT method to train a vision backbone model to classify histology images by pairing them with privileged textual information from an external source. At first, the modality pairing step relies on a CLIP-based model to match histology images with semantically relevant textual report data from external sources, creating an augmented multimodal dataset without the need for manually paired samples. Then, we propose a multimodal training procedure that distills the knowledge from the paired text modality to the unimodal image classifier for enhanced performance without the need for the textual data during inference. A parameter-efficient fine-tuning method is used to efficiently address the misalignment between the main (image) and paired (text) modalities. During inference, the improved unimodal histology classifier is used, with only minimal additional computational complexity. Our experiments on challenging PCAM, CRC, and BACH histology image datasets show that CLIP-IT can provide a cost-effective approach to leverage privileged textual information and outperform unimodal classifiers for histology.

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