Decoding the Black Box : Enhancing Interpretability and Trust in Artificial Intelligence for Biomedical Imaging - a Step Toward Responsible Artificial Intelligence

Abstract

In an era dominated by AI, its opaque decision-making --known as the "black box" problem-- poses significant challenges, especially in critical areas like biomedical imaging where accuracy and trust are crucial. Our research focuses on enhancing AI interpretability in biomedical applications. We have developed a framework for analyzing biomedical images that quantifies phagocytosis in neurodegenerative diseases using time-lapse phase-contrast video microscopy. Traditional methods often struggle with rapid cellular interactions and distinguishing cells from backgrounds, critical for studying conditions like frontotemporal dementia (FTD). Our scalable, real-time framework features an explainable cell segmentation module that simplifies deep learning algorithms, enhances interpretability, and maintains high performance by incorporating visual explanations and by model simplification. We also address issues in visual generative models, such as hallucinations in computational pathology, by using a unique encoder for Hematoxylin and Eosin staining coupled with multiple decoders. This method improves the accuracy and reliability of synthetic stain generation, employing innovative loss functions and regularization techniques that enhance performance and enable precise synthetic stains crucial for pathological analysis. Our methodologies have been validated against several public benchmarks, showing top-tier performance. Notably, our framework distinguished between mutant and control microglial cells in FTD, providing new biological insights into this unproven phenomenon. Additionally, we introduced a cloud-based system that integrates complex models and provides real-time feedback, facilitating broader adoption and iterative improvements through pathologist insights. The release of novel datasets, including video microscopy on microglial cell phagocytosis and a virtual staining dataset related to pediatric Crohn's disease, along with all source codes, underscores our commitment to transparent open scientific collaboration and advancement. Our research highlights the importance of interpretability in AI, advocating for technology that integrates seamlessly with user needs and ethical standards in healthcare. Enhanced interpretability allows researchers to better understand data and improve tool performance.

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