Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images

2021 ◽  
Vol 26 (1) ◽  
pp. 93-102
Author(s):  
Yue Zhang ◽  
Shijie Liu ◽  
Chunlai Li ◽  
Jianyu Wang
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Lesion Segmentation

scholarly journals Pathological myopia classification with simultaneous lesion segmentation using deep learning

2021 ◽  
Vol 199 ◽  
pp. 105920
Author(s):  
Ruben Hemelings ◽  
Bart Elen ◽  
Matthew B. Blaschko ◽  
Julie Jacob ◽  
Ingeborg Stalmans ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lesion Segmentation ◽  
Pathological Myopia

Overview of Deep Learning Algorithms Applied to Medical Images

2019 ◽  
pp. 225-237
Author(s):  
Behnaz Abdollahi ◽  
Ayman El-Baz ◽  
Hermann B. Frieboes
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Learning Algorithms

scholarly journals Accelerating Super-Resolution and Visual Task Analysis in Medical Images

2020 ◽  
Vol 10 (12) ◽  
pp. 4282
Author(s):  
Ghada Zamzmi ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Task Analysis ◽  
Multiple Scales ◽  
Medical Images ◽  
Computational Cost ◽  
Super Resolution ◽  
Visual Task ◽  
Learning Networks ◽  
High Resolution Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

scholarly journals COVID-19 prediction using chest X-ray medical images and Deep Learning

2021 ◽  
Author(s):  
Nuria Pereira Espasandín ◽  
David Maseda Neira ◽  
Diana Marcela Noriega Cobo ◽  
Iago Iglesias Corrás ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
X Ray ◽  
Chest X Ray

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials

Radiology ◽  
2021 ◽  
Author(s):  
Anitha Priya Krishnan ◽  
Zhuang Song ◽  
David Clayton ◽  
Laura Gaetano ◽  
Xiaoming Jia ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation
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