Holger R. Roth

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 18 papers; trusts 592 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

Computer Vision and Pattern Recognition — 323 citations into it; #8,071 of 21,575 by trust here, #1,912 by citations
Artificial Intelligence — 252 citations into it; #28,643 of 35,345 by trust here, #4,956 by citations
Radiology, Nuclear Medicine and Imaging — 119 citations into it; #626 of 3,265 by trust here, #73 by citations
Model Architectures — 67 citations into it
Biomedical Engineering — 43 citations into it
Representation & Self-Supervised Learning — 38 citations into it
Optimization & Training — 30 citations into it
Neurology — 24 citations into it

Whom Holger R. Roth trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Holger R. Roth overall

Papers from Holger R. Roth's perspective

Ordered by how strongly the researchers Holger R. Roth trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
ImageNet: A large-scale hierarchical image database
Attention is All you Need (Neural Information Processing Systems)
Long Short-Term Memory
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
ImageNet Large Scale Visual Recognition Challenge
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2016) — 5,088 citations
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation (2015) — 870 citations
UNETR: Transformers for 3D Medical Image Segmentation (2021) — 2,828 citations
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (2022) — 2,376 citations
Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆ (2017) — 300 citations
The future of digital health with federated learning (2020) — 2,960 citations
An application of cascaded 3D fully convolutional networks for medical image segmentation (2018) — 295 citations
MONAI: An open-source framework for deep learning in healthcare (2022) — 996 citations
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis (2021) — 876 citations
Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) — 736 citations
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020) — 273 citations
Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation (2020) — 514 citations
Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets (2020) — 535 citations
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation (2019) — 50 citations
Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks (2017) — 66 citations
Deep convolutional networks for automated detection of posterior-element fractures on spine CT (2016) — 58 citations
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015) — 585 citations
A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations (2014) — 580 citations