Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification
Journal:
arXiv
Published Date:
May 21, 2025
Abstract
Deep learning has advanced computational pathology but expert annotations
remain scarce. Few-shot learning mitigates annotation burdens yet suffers from
overfitting and discriminative feature mischaracterization. In addition, the
current few-shot multiple instance learning (MIL) approaches leverage
pretrained vision-language models to alleviate these issues, but at the cost of
complex preprocessing and high computational cost. We propose a
Squeeze-and-Recalibrate (SR) block, a drop-in replacement for linear layers in
MIL models to address these challenges. The SR block comprises two core
components: a pair of low-rank trainable matrices (squeeze pathway, SP) that
reduces parameter count and imposes a bottleneck to prevent spurious feature
learning, and a frozen random recalibration matrix that preserves geometric
structure, diversifies feature directions, and redefines the optimization
objective for the SP. We provide theoretical guarantees that the SR block can
approximate any linear mapping to arbitrary precision, thereby ensuring that
the performance of a standard MIL model serves as a lower bound for its
SR-enhanced counterpart. Extensive experiments demonstrate that our SR-MIL
models consistently outperform prior methods while requiring significantly
fewer parameters and no architectural changes.
