activity
20182022
most citedGifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays

21 citations · 47 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV20226 cited

Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks

Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler +9

We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale…

eess.IV2022

Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning

Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai +8

Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medic…

eess.IV20221 cited

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

Beliz Gunel, Arda Sahiner, Arjun D. Desai +4

Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.…

eess.IV2022

SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

Arjun D Desai, Andrew M Schmidt, Elka B Rubin +9

Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and d…

eess.IV20212 cited

OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations

Anirudh Joshi, Sabri Eyuboglu, Shih-Cheng Huang +5

FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches…

cs.CV202121 cited

Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays

Joseph Paul Cohen, Rupert Brooks, Sovann En +4

Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imag…