128 citations · 246 across the 23 of their papers we have counts for
27 papers
EvoCowork: A Benchmark for Real-World Office Tasks
Zhou Liu, Xingyan Chen, ZhaoYang Han +6
Office workflows are a demanding testbed for self-improving agents because their deliverables must be correct, natively editable, and mutually consistent. A central question is whe…
UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound
Ashwath Radhachandran, Adam Tupper, Christian Gagné +1
Benchmarking is an increasingly critical part of research in machine learning and the domains where it is applied, including healthcare. Yet, despite the steady development of new…
TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance
Hongye Zeng, Shreeram Athreya, Dingyuan Dai +4
Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression…
Pretext Matters: An Empirical Study of SSL Methods in Medical Imaging
Vedrana Ivezić, Mara Pleasure, Ashwath Radhachandran +7
Though self-supervised learning (SSL) has demonstrated incredible ability to learn robust representations from unlabeled data, the choice of optimal SSL strategy can lead to vastly…
US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound
Ashwath Radhachandran, Vedrana Ivezić, Shreeram Athreya +2
Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process. The low signal-to-noise ratio and stochastic speckle pa…
Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
Mara Pleasure, Ekaterina Redekop, Dhakshina Ilango +9
Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep lea…