[Retracted] Deep Neural Networks for Medical Image Segmentation
P Malhotra, S Gupta, D Koundal… - Journal of …, 2022 - Wiley Online Library
Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …
applications in the field of analysis of images, augmented reality, machine vision, and many …
Brain tumor detection and classification using machine learning: a comprehensive survey
J Amin, M Sharif, A Haldorai, M Yasmin… - Complex & intelligent …, 2022 - Springer
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI
Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Unetr: Transformers for 3d medical image segmentation
Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications …
paths have shown prominence for the majority of medical image segmentation applications …
Universeg: Universal medical image segmentation
While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
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 …
Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no
control over tumor growth. Deep learning has been argued to have the potential to …
control over tumor growth. Deep learning has been argued to have the potential to …