Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation?

Autor:	Elphick, Matouš, Turajlic, Samra, Yang, Guang
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Self-supervised foundation models for digital pathology encode small patches from H\&E whole slide images into latent representations used for downstream tasks. However, the invariance of these representations to patch rotation remains unexplored. This study investigates the rotational invariance of latent representations across twelve foundation models by quantifying the alignment between non-rotated and rotated patches using mutual $k$-nearest neighbours and cosine distance. Models that incorporated rotation augmentation during self-supervised training exhibited significantly greater invariance to rotations. We hypothesise that the absence of rotational inductive bias in the transformer architecture necessitates rotation augmentation during training to achieve learned invariance. Code: https://github.com/MatousE/rot-invariance-analysis. Comment: Samra Turajlic and Guang Yang are joint last authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.11938 Zobrazit plný text záznamu View this record from Arxiv