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...
O. Dzikunu, Mohammad Mahdi Abootorabi, Mohamed Harmanani et al.· 0 citations
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...
O. Dzikunu, E. Willis, Mohammad Mahdi Abootorabi et al.· 0 citations
Position-prompted PCA (P3CA), an encoder-agnostic method for local probing of channel-rich spatial tensors, is proposed and implemented in EmbedVision, an interactive 3D Slicer-based workflow, and evaluated across natural images, colorectal pathology foundation-model embeddings, and spatial transcriptomic tensors.
A. Jamzad, Dilakshan Srikanthan, F. Akbarifar et al.· 0 citations
This study demonstrates promising potential for using AI to build an automatic patient monitoring system using feature-based classifiers, deep learning, and ECG foundation models across three data-driven artificial intelligence approaches for high-performing AF detection.
Sarah Nassar, N. Maghsoodi, Sophia Mannina et al.· IEEE transactions on bio-med...· 0 citations
As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated behaviors, leaving it u...
Mohammad Mahdi Abootorabi, Armin Saghafian, Ali Bazshoushtari et al.· 0 citations
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