Report
MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological images
| العنوان: | MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological images |
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| المؤلفون: | Meseguer, Pablo, del Amor, Rocío, Naranjo, Valery |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| الوصف: | Vision-language supervision has made remarkable strides in learning visual representations from textual guidance. In digital pathology, vision-language models (VLM), pre-trained on curated datasets of histological image-captions, have been adapted to downstream tasks, such as region of interest classification. Zero-shot transfer for slide-level prediction has been formulated by MI-Zero, but it exhibits high variability depending on the textual prompts. Inspired by prototypical learning, we propose MI-VisionShot, a training-free adaptation method on top of VLMs to predict slide-level labels in few-shot learning scenarios. Our framework takes advantage of the excellent representation learning of VLM to create prototype-based classifiers under a multiple-instance setting by retrieving the most discriminative patches within each slide. Experimentation through different settings shows the ability of MI-VisionShot to surpass zero-shot transfer with lower variability, even in low-shot scenarios. Code coming soon at thttps://github.com/cvblab/MIVisionShot. Comment: Manuscript accepted for oral presentation at KES-InnovationInMedicine 2024 held on Madeira, Portugal |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2410.15881 |
| رقم الانضمام: | edsarx.2410.15881 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |