PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imaging
This addresses the need for interpretability in DNNs for biomedical applications, though it is an incremental improvement over existing attribution methods.
The authors tackled the problem of interpreting deep neural network decisions in biomedical imaging by introducing PCIM, a method for generating pixel attribution maps without needing internal network states or gradients, achieving state-of-the-art performance in model fidelity and localization on three high-content imaging datasets.
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.