Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …
segmentation models based on convolutional neural networks. Despite the new …
Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review
Being a second most cause of mortality worldwide, cancer has been identified as a perilous
disease for human beings, where advance stage diagnosis may not help much in …
disease for human beings, where advance stage diagnosis may not help much in …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss
Knee osteoarthritis (OA) is one major cause of activity limitation and physical disability in
older adults. Early detection and intervention can help slow down the OA degeneration …
older adults. Early detection and intervention can help slow down the OA degeneration …
Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches
Risk stratification (characterization) of tumors from radiology images can be more accurate
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
High-resolution encoder–decoder networks for low-contrast medical image segmentation
Automatic image segmentation is an essential step for many medical image analysis
applications, include computer-aided radiation therapy, disease diagnosis, and treatment …
applications, include computer-aided radiation therapy, disease diagnosis, and treatment …
[HTML][HTML] Large-scale multi-center CT and MRI segmentation of pancreas with deep learning
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed
for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic …
for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic …
Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3D intraoral scanners
Precisely labeling teeth on digitalized 3D dental surface models is the precondition for tooth
position rearrangements in orthodontic treatment planning. However, it is a challenging task …
position rearrangements in orthodontic treatment planning. However, it is a challenging task …