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 …
Deep semantic segmentation of natural and medical images: a review
The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …
instance, where each instance corresponds to a class. This task is a part of the concept of …
Searching efficient 3d architectures with sparse point-voxel convolution
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive
safely. Given the limited hardware resources, existing 3D perception models are not able to …
safely. Given the limited hardware resources, existing 3D perception models are not able to …
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …
introduced into the optimization community. BLO is able to handle problems with a …
[HTML][HTML] Volumetric memory network for interactive medical image segmentation
Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …
Deep learning based brain tumor segmentation: a survey
Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
Enable deep learning on mobile devices: Methods, systems, and applications
Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …
intelligence (AI), including computer vision, natural language processing, and speech …
Continual segment: Towards a single, unified and non-forgetting continual segmentation model of 143 whole-body organs in ct scans
Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …
Nevertheless, current deep segmentation approaches are not capable of efficiently and …
[LIVRE][B] Deep learning for medical image analysis
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …
academic and industry researchers and graduate students taking courses on machine …