ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer

TL;DR

ProjectedEx introduces a novel generative framework that improves interpretability of AI in prostate cancer diagnosis by linking image features to classifier decisions, with enhanced multiscale feedback for better explanations.

Contribution

It presents a new framework, ProjectedEx, that provides interpretable, multi-attribute explanations for medical imaging AI, addressing limitations of existing GAN-based methods in healthcare.

Findings

Demonstrates clinical relevance of explanations

Improves interpretability of prostate cancer diagnosis

Enhances explanation quality with multiscale feedback

Abstract

Prostate cancer, a growing global health concern, necessitates precise diagnostic tools, with Magnetic Resonance Imaging (MRI) offering high-resolution soft tissue imaging that significantly enhances diagnostic accuracy. Recent advancements in explainable AI and representation learning have significantly improved prostate cancer diagnosis by enabling automated and precise lesion classification. However, existing explainable AI methods, particularly those based on frameworks like generative adversarial networks (GANs), are predominantly developed for natural image generation, and their application to medical imaging often leads to suboptimal performance due to the unique characteristics and complexity of medical image. To address these challenges, our paper introduces three key contributions. First, we propose ProjectedEx, a generative framework that provides interpretable, multi-attribute explanations, effectively linking medical image features to classifier decisions. Second, we enhance the encoder module by incorporating feature pyramids, which enables multiscale feedback to refine the latent space and improves the quality of generated explanations. Additionally, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx in enhancing interpretability and supporting the adoption of AI in medical settings. Code will be released at https://github.com/Richardqiyi/ProjectedEx