29 citations · 42 across the 6 of their papers we have counts for
8 papers
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation
Aliasghar Mortazi, Vedat Cicek, Elif Keles +1
The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networ…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
Weakly Supervised Segmentation by A Deep Geodesic Prior
Aliasghar Mortazi, Naji Khosravan, Drew A. Torigian +2
The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. T…
PAN: Projective Adversarial Network for Medical Image Segmentation
Naji Khosravan, Aliasghar Mortazi, Michael Wallace +1
Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D s…
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
Xiahai Zhuang, Lei Li, Christian Payer +31
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable…
Automatically Designing CNN Architectures for Medical Image Segmentation
Aliasghar Mortazi, Ulas Bagci
Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of…