activity
20152023
most citedMONAI: An open-source framework for deep learning in healthcare

452 citations · 1.4k across the 40 of their papers we have counts for

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Showing eess.IVShow all

15 papers · 1 filter

eess.IV2023

DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images

Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle +17

Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be te…

eess.IV202230 cited

UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

Ali Hatamizadeh, Ziyue Xu, Dong Yang +3

Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to datas…

eess.IV202237 cited

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang +3

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient an…

eess.IV20211 cited

Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging

Andriy Myronenko, Ziyue Xu, Dong Yang +2

Multiple instance learning (MIL) is a key algorithm for classification of whole slide images (WSI). Histology WSIs can have billions of pixels, which create enormous computational…

eess.IV20211 cited

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

Holger R. Roth, Dong Yang, Wenqi Li +5

Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privac…

eess.IV2021

The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation

Vishwesh Nath, Dong Yang, Ali Hatamizadeh +4

Deep learning models for medical image segmentation are primarily data-driven. Models trained with more data lead to improved performance and generalizability. However, training is…