Conventional and deep learning methods for skull strip** in brain MRI
Featured Application Skull strip** is the most prevalent brain image analysis method. This
method can be applied to areas such as brain tissue segmentation and volumetric …
method can be applied to areas such as brain tissue segmentation and volumetric …
A survey on shape-constraint deep learning for medical image segmentation
S Bohlender, I Oksuz… - IEEE Reviews in …, 2021 - ieeexplore.ieee.org
Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …
have completely changed the modern landscape of deep-learning based medical image …
Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation
A brain tumor is one of the fatal cancer types which causes abnormal growth of brain cells.
Earlier diagnosis of a brain tumor can play a vital role in its treatment; however, manual …
Earlier diagnosis of a brain tumor can play a vital role in its treatment; however, manual …
3D U-Net for skull strip** in brain MRI
Skull strip** in brain magnetic resonance imaging (MRI) is an essential step to analyze
images of the brain. Although manual segmentation has the highest accuracy, it is a time …
images of the brain. Although manual segmentation has the highest accuracy, it is a time …
Joint self-supervised image-volume representation learning with intra-inter contrastive clustering
Collecting large-scale medical datasets with fully annotated samples for training of deep
networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in …
networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in …
Medical image segmentation with UNet-based multi-scale context fusion
Y Yuan, Y Cheng - Scientific Reports, 2024 - nature.com
Histopathological examination holds a crucial role in cancer grading and serves as a
significant reference for devising individualized patient treatment plans in clinical practice …
significant reference for devising individualized patient treatment plans in clinical practice …
State-of-the-art traditional to the machine-and deep-learning-based skull strip** techniques, models, and algorithms
Several neuroimaging processing applications consider skull strip** as a crucial pre-
processing step. Due to complex anatomical brain structure and intensity variations in brain …
processing step. Due to complex anatomical brain structure and intensity variations in brain …
A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging
Automated medical image segmentation is an essential task to aid/speed up diagnosis and
treatment procedures in clinical practices. Deep convolutional neural networks have …
treatment procedures in clinical practices. Deep convolutional neural networks have …
Selective deeply supervised multi-scale attention network for brain tumor segmentation
Brain tumors are among the deadliest forms of cancer, characterized by abnormal
proliferation of brain cells. While early identification of brain tumors can greatly aid in their …
proliferation of brain cells. While early identification of brain tumors can greatly aid in their …
TATL: Task agnostic transfer learning for skin attributes detection
Existing skin attributes detection methods usually initialize with a pre-trained Imagenet
network and then fine-tune on a medical target task. However, we argue that such …
network and then fine-tune on a medical target task. However, we argue that such …