An overview of deep learning in medical imaging focusing on MRI
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering
performance in a variety of computer vision problems, such as visual object recognition …
performance in a variety of computer vision problems, such as visual object recognition …
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 …
[HTML][HTML] TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …
different challenges compared to RGB images typically used in computer vision. These …
Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …
art performance in the last few years. More specifically, these techniques have been …
CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation
Accurate medical image segmentation is essential for diagnosis and treatment planning of
diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art …
diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art …
Cross-modality deep feature learning for brain tumor segmentation
Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations
Deep-learning has proved in recent years to be a powerful tool for image analysis and is
now widely used to segment both 2D and 3D medical images. Deep-learning segmentation …
now widely used to segment both 2D and 3D medical images. Deep-learning segmentation …
[HTML][HTML] Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Despite the state-of-the-art performance for medical image segmentation, deep
convolutional neural networks (CNNs) have rarely provided uncertainty estimations …
convolutional neural networks (CNNs) have rarely provided uncertainty estimations …
Interactive medical image segmentation using deep learning with image-specific fine tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for
automatic medical image segmentation. However, they have not demonstrated sufficiently …
automatic medical image segmentation. However, they have not demonstrated sufficiently …