22 citations · 22 across the 2 of their papers we have counts for
5 papers
Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks
Victor Ibañez, Przemyslaw Szostak, Quincy Wong +3
Graph convolutional networks (GCNs) have emerged as a powerful alternative to multiple instance learning with convolutional neural networks in digital pathology, offering superior…
The Importance of Downstream Networks in Digital Pathology Foundation Models
Gustav Bredell, Marcel Fischer, Przemyslaw Szostak +2
Digital pathology has significantly advanced disease detection and pathologist efficiency through the analysis of gigapixel whole-slide images (WSI). In this process, WSIs are firs…
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil +4
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with…
Risk of Training Diagnostic Algorithms on Data with Demographic Bias
Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels +2
One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algor…
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz +3
We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mec…