  Early detection of many life-threatening diseases (e.g., prostate and breast
cancer) within at-risk population can improve clinical outcomes and reduce cost
of care. While numerous disease-specific "screening" tests that are closer to
Point-of-Care (POC) are in use for this task, their low specificity results in
unnecessary biopsies, leading to avoidable patient trauma and wasteful
healthcare spending. On the other hand, despite the high accuracy of Magnetic
Resonance (MR) imaging in disease diagnosis, it is not used as a POC disease
identification tool because of poor accessibility. The root cause of poor
accessibility of MR stems from the requirement to reconstruct high-fidelity
images, as it necessitates a lengthy and complex process of acquiring large
quantities of high-quality k-space measurements. In this study we explore the
feasibility of an ML-augmented MR pipeline that directly infers the disease
sidestepping the image reconstruction process. We hypothesise that the disease
classification task can be solved using a very small tailored subset of k-space
data, compared to image reconstruction. Towards that end, we propose a method
that performs two tasks: 1) identifies a subset of the k-space that maximizes
disease identification accuracy, and 2) infers the disease directly using the
identified k-space subset, bypassing the image reconstruction step. We validate
our hypothesis by measuring the performance of the proposed system across
multiple diseases and anatomies. We show that comparable performance to
image-based classifiers, trained on images reconstructed with full k-space
data, can be achieved using small quantities of data: 8% of the data for
detecting multiple abnormalities in prostate and brain scans, and 5% of the
data for knee abnormalities. To better understand the proposed approach and
instigate future research, we provide an extensive analysis and release code.
