Medical image segmentation review: The success of u-net
Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
Transmed: Transformers advance multi-modal medical image classification
Y Dai, Y Gao, F Liu - Diagnostics, 2021 - mdpi.com
Over the past decade, convolutional neural networks (CNN) have shown very competitive
performance in medical image analysis tasks, such as disease classification, tumor …
performance in medical image analysis tasks, such as disease classification, tumor …
nnU-Net for brain tumor segmentation
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified
nnU-Net baseline configuration already achieves a respectable result. By incorporating …
nnU-Net baseline configuration already achieves a respectable result. By incorporating …
Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists
Manual identification of brain tumors is an error-prone and tedious process for radiologists;
therefore, it is crucial to adopt an automated system. The binary classification process, such …
therefore, it is crucial to adopt an automated system. The binary classification process, such …
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Labeled medical imaging data is scarce and expensive to generate. To achieve
generalizable deep learning models large amounts of data are needed. Standard data …
generalizable deep learning models large amounts of data are needed. Standard data …
3D deep learning on medical images: a review
The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …
availability of medical imaging data have led to a rapid increase in the use of deep learning …
[HTML][HTML] A review: Deep learning for medical image segmentation using multi-modality fusion
Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
nnu-net: Self-adapting framework for u-net-based medical image segmentation
The U-Net was presented in 2015. With its straight-forward and successful architecture it
quickly evolved to a commonly used benchmark in medical image segmentation. The …
quickly evolved to a commonly used benchmark in medical image segmentation. The …
Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation
Recent advances in deep learning for medical image segmentation demonstrate expert-
level accuracy. However, application of these models in clinically realistic environments can …
level accuracy. However, application of these models in clinically realistic environments can …