A review on deep learning in medical image analysis
Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …
Transfer learning techniques for medical image analysis: A review
Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
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 …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Be your own teacher: Improve the performance of convolutional neural networks via self distillation
L Zhang, J Song, A Gao, J Chen… - Proceedings of the …, 2019 - openaccess.thecvf.com
Convolutional neural networks have been widely deployed in various application scenarios.
In order to extend the applications' boundaries to some accuracy-crucial domains …
In order to extend the applications' boundaries to some accuracy-crucial domains …
MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation
Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …
[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 …
Reducing the hausdorff distance in medical image segmentation with convolutional neural networks
D Karimi, SE Salcudean - IEEE Transactions on medical …, 2019 - ieeexplore.ieee.org
The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation
methods. However, the existing segmentation methods do not attempt to reduce HD directly …
methods. However, the existing segmentation methods do not attempt to reduce HD directly …
Boundary loss for highly unbalanced segmentation
Widely used loss functions for convolutional neural network (CNN) segmentation, eg, Dice
or cross-entropy, are based on integrals (summations) over the segmentation regions …
or cross-entropy, are based on integrals (summations) over the segmentation regions …
Self-distillation: Towards efficient and compact neural networks
Remarkable achievements have been obtained by deep neural networks in the last several
years. However, the breakthrough in neural networks accuracy is always accompanied by …
years. However, the breakthrough in neural networks accuracy is always accompanied by …