Exploring Foundation Models Fine-Tuning for Cytology Classification

Autor:	Dausort, Manon, Godelaine, Tiffanie, Zanella, Maxime, Khoury, Karim El, Salmon, Isabelle, Macq, Benoît
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	Cytology slides are essential tools in diagnosing and staging cancer, but their analysis is time-consuming and costly. Foundation models have shown great potential to assist in these tasks. In this paper, we explore how existing foundation models can be applied to cytological classification. More particularly, we focus on low-rank adaptation, a parameter-efficient fine-tuning method suited to few-shot learning. We evaluated five foundation models across four cytological classification datasets. Our results demonstrate that fine-tuning the pre-trained backbones with LoRA significantly improves model performance compared to fine-tuning only the classifier head, achieving state-of-the-art results on both simple and complex classification tasks while requiring fewer data samples. Comment: 5 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.14975 Zobrazit plný text záznamu View this record from Arxiv