A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Artificial intelligence and acute stroke imaging
JE Soun, DS Chow, M Nagamine… - American Journal …, 2021 - Am Soc Neuroradiology
Artificial intelligence technology is a rapidly expanding field with many applications in acute
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
[HTML][HTML] Deep learning for topology optimization of 2D metamaterials
HT Kollmann, DW Abueidda, S Koric, E Guleryuz… - Materials & Design, 2020 - Elsevier
Data-driven models are rising as an auspicious method for the geometrical design of
materials and structural systems. Nevertheless, existing data-driven models customarily …
materials and structural systems. Nevertheless, existing data-driven models customarily …
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Imaging through scattering is an important yet challenging problem. Tremendous progress
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …
Non-local deep features for salient object detection
Saliency detection aims to highlight the most relevant objects in an image. Methods using
conventional models struggle whenever salient objects are pictured on top of a cluttered …
conventional models struggle whenever salient objects are pictured on top of a cluttered …
[HTML][HTML] Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
Abstract Background Medical Image segmentation is an important image processing step.
Comparing images to evaluate the quality of segmentation is an essential part of measuring …
Comparing images to evaluate the quality of segmentation is an essential part of measuring …
Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and …
B Norman, V Pedoia, S Majumdar - Radiology, 2018 - pubs.rsna.org
Purpose To analyze how automatic segmentation translates in accuracy and precision to
morphology and relaxometry compared with manual segmentation and increases the speed …
morphology and relaxometry compared with manual segmentation and increases the speed …
The multimodal brain tumor image segmentation benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review
Advances in artificial intelligence-based methods have led to the development and
publication of numerous systems for auto-segmentation in radiotherapy. These systems …
publication of numerous systems for auto-segmentation in radiotherapy. These systems …
Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey
N Asiri, M Hussain, F Al Adel, N Alzaidi - Artificial intelligence in medicine, 2019 - Elsevier
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided
diagnosis (CAD) system based on retinal fundus images is an efficient and effective method …
diagnosis (CAD) system based on retinal fundus images is an efficient and effective method …