452 citations · 1k across the 29 of their papers we have counts for
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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…
RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging
Ali Hatamizadeh, Hamid Hosseini, Niraj Patel +5
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary in…
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…
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…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
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…
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…
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…
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images
Ali Hatamizadeh, Demetri Terzopoulos, Andriy Myronenko
Textures and edges contribute different information to image recognition. Edges and boundaries encode shape information, while textures manifest the appearance of regions. Despite…
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
Andriy Myronenko, Ali Hatamizadeh
Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation. Tumor segmentation is one of the…
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks
Andriy Myronenko, Ali Hatamizadeh
Automated segmentation of kidneys and kidney tumors is an important step in quantifying the tumor's morphometrical details to monitor the progression of the disease and accurately…
Deep Active Lesion Segmentation
Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta +4
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable…
Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
Ali Hatamizadeh, Hamid Hosseini, Zhengyuan Liu +2
The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, includin…