Adam Kinnaird

MUSegNet: Automated detection of clinically significant prostate cancer on micro-ultrasound.

Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound

Domain shift across clinical centers using different imaging hardware or acquisition protocols remains a fundamental barrier to deploying deep learning models for prostate cancer (PCa) detection. Existing test-time adaptation (TTA) methods address distribution shift through entropy minimization or augmentation-based se...

Data-Efficient Prostate Micro-Ultrasound Image Analysis using a Modality-specific Foundation Model

Background and Objective: Micro-ultrasound (MicroUS) is an emerging high-resolution imaging modality for prostate cancer diagnosis and real-time biopsy guidance, but its automated analysis is limited by labor-intensive expert annotation. We aimed to develop and evaluate a foundation model for data-efficient MicroUS ima...

Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading

Unpaired cross-modal distillation transfers grade structure from histopathology into a micro-ultrasound (micro-US) encoder by aligning a pooled needle-region embedding to a frozen histopathology teacher under grade-group correspondence alone. A single objective is thereby required to serve two distinct functions: rende...

Pharmacologic Targeting of ZNF281 Suppresses Metastatic Prostate Cancer Beyond Androgen Receptor Dependence