23 citations · 52 across the 6 of their papers we have counts for
6 papers
RadEval: A framework for radiology text evaluation
Justin Xu, Xi Zhang, Javid Abderezaei +9
We introduce RadEval, a unified, open-source framework for evaluating radiology texts. RadEval consolidates a diverse range of metrics, from classic n-gram overlap (BLEU, ROUGE) an…
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway +1
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing…
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Michael B. Gotway +1
Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given th…
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Ruibin Feng +2
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of…
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imagi…
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2
Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster…