Improving Clinical Imaging Systems using Cognition based Approaches

Abstract

Clinical systems operate in safety-critical environments and are not intended to function autonomously; however, they are currently designed to replicate clinicians' diagnoses rather than assist them in the diagnostic process. To enable better supervision of system-generated diagnoses, we replicate radiologists' systematic approach used to analyze chest X-rays. This approach facilitates comprehensive analysis across all regions of clinical images and can reduce errors caused by inattentional blindness and under reading. Our work addresses a critical research gap by identifying difficult-to-diagnose diseases for clinicians using insights from human vision, enabling these systems to serve as an effective "second pair of eyes". These improvements make the clinical imaging systems more complementary and combine the strengths of human and machine vision. Additionally, we leverage effective receptive fields in deep learning models to present machine-generated diagnoses with sufficient context, making it easier for clinicians to evaluate them.

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