activity
20182020
most citedA large annotated medical image dataset for the development and evaluation of segmentation algorithms

718 citations · 849 across the 5 of their papers we have counts for

collaborators

9 papers

eess.IV20202 cited

A Deep Network for Joint Registration and Reconstruction of Images with Pathologies

Xu Han, Zhengyang Shen, Zhenlin Xu +5

Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed fo…

cs.CY2020

The Future of Digital Health with Federated Learning

Nicola Rieke, Jonny Hancox, Wenqi Li +14

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern…

q-bio.QM2020

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Andreas Mang, Spyridon Bakas, Shashank Subramanian +2

Central nervous system (CNS) tumors come with the vastly heterogeneous histologic, molecular and radiographic landscape, rendering their precise characterization challenging. The r…

cs.LG20199 cited

ModelHub.AI: Dissemination Platform for Deep Learning Models

Ahmed Hosny, Michael Schwier, Christoph Berger +13

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among…

eess.IV20196 cited

Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool

Ludovic Venet, Sarthak Pati, Paul Yushkevich +1

Variously stained histology slices are routinely used by pathologists to assess extracted tissue samples from various anatomical sites and determine the presence or extent of a dis…

cs.CV2019718 cited

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Amber L. Simpson, Michela Antonelli, Spyridon Bakas +21

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is c…