PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imaging

TL;DR

This paper introduces PCIM, a novel pixel attribution method for deep neural networks in high content imaging, which generates interpretable pixel importance maps without requiring network internal states or gradients.

Contribution

PCIM is a new approach that treats each pixel as a separate input channel and trains a blending layer, enabling pixel attribution maps for any classifier without internal network modifications.

Findings

Achieves state-of-the-art performance in fidelity and localization on diverse imaging datasets.

Works effectively across fluorescence and bright field high content imaging.

Provides interpretable pixel importance maps independent of the underlying model.

Abstract

Deep Neural Networks (DNNs) have shown remarkable success in various computer vision tasks. However, their black-box nature often leads to difficulty in interpreting their decisions, creating an unfilled need for methods to explain the decisions, and ultimately forming a barrier to their wide acceptance especially in biomedical applications. This work introduces a novel method, Pixel-wise Channel Isolation Mixing (PCIM), to calculate pixel attribution maps, highlighting the image parts most crucial for a classification decision but without the need to extract internal network states or gradients. Unlike existing methods, PCIM treats each pixel as a distinct input channel and trains a blending layer to mix these pixels, reflecting specific classifications. This unique approach allows the generation of pixel attribution maps for each image, but agnostic to the choice of the underlying classification network. Benchmark testing on three application relevant, diverse high content Imaging datasets show state-of-the-art performance, particularly for model fidelity and localization ability in both, fluorescence and bright field High Content Imaging. PCIM contributes as a unique and effective method for creating pixel-level attribution maps from arbitrary DNNs, enabling interpretability and trust.