8 citations · 9 across the 5 of their papers we have counts for
6 papers
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…
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…
CytoFM: The first cytology foundation model
Vedrana Ivezić, Ashwath Radhachandran, Ekaterina Redekop +5
Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is cha…
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model
Shreeram Athreya, Andrew Melehy, Sujit Silas Armstrong Suthahar +9
Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only…
Ultrasound Image Enhancement using CycleGAN and Perceptual Loss
Shreeram Athreya, Ashwath Radhachandran, Vedrana Ivezić +3
Purpose: The objective of this work is to introduce an advanced framework designed to enhance ultrasound images, especially those captured by portable hand-held devices, which ofte…