3.1k citations · 7.2k across the 83 of their papers we have counts for
29 papers · 1 filter
Text-Driven Tumor Synthesis
Xinran Li, Yi Shuai, Chen Liu +11
Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases. However, existing synthesis methods, wh…
MAISI: Medical AI for Synthetic Imaging
Pengfei Guo, Can Zhao, Dong Yang +8
Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an…
HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization
Yucheng Tang, Yufan He, Vishwesh Nath +11
In digital pathology, the traditional method for deep learning-based image segmentation typically involves a two-stage process: initially segmenting high-resolution whole slide ima…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge
Andriy Myronenko, Dong Yang, Yufan He +1
Aorta provides the main blood supply of the body. Screening of aorta with imaging helps for early aortic disease detection and monitoring. In this work, we describe our solution to…
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…
Automated head and neck tumor segmentation from 3D PET/CT
Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang +2
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET…
Automated segmentation of intracranial hemorrhages from 3D CT
Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He +2
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs…
Automated ischemic stroke lesion segmentation from 3D MRI
Md Mahfuzur Rahman Siddique, Dong Yang, Yufan He +2
Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs.…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
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…
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Pengfei Guo, Dong Yang, Ali Hatamizadeh +14
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving…
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…
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Cheng Peng, Andriy Myronenko, Ali Hatamizadeh +6
Semantic segmentation of 3D medical images is a challenging task due to the high variability of the shape and pattern of objects (such as organs or tumors). Given the recent succes…
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Dong Yang, Andriy Myronenko, Xiaosong Wang +3
Lesion segmentation in medical imaging has been an important topic in clinical research. Researchers have proposed various detection and segmentation algorithms to address this tas…
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…
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam +4
Localization and characterization of diseases like pneumonia are primary steps in a clinical pipeline, facilitating detailed clinical diagnosis and subsequent treatment planning. A…
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…
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…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Yingda Xia, Dong Yang, Wenqi Li +15
Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standar…
UNETR: Transformers for 3D Medical Image Segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath +5
Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past de…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Pochuan Wang, Chen Shen, Holger R. Roth +9
The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image an…