Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
[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 …
[HTML][HTML] Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
Brain tumor classification for MR images using transfer learning and fine-tuning
Accurate and precise brain tumor MR images classification plays important role in clinical
diagnosis and decision making for patient treatment. The key challenge in MR images …
diagnosis and decision making for patient treatment. The key challenge in MR images …
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
[HTML][HTML] Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline
Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …
consuming optimization steps, and thus, do not scale well to large cohort studies with …
Deep learning applications in medical image analysis
The tremendous success of machine learning algorithms at image recognition tasks in
recent years intersects with a time of dramatically increased use of electronic medical …
recent years intersects with a time of dramatically increased use of electronic medical …
Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images
Segmentation of livers and liver tumors is one of the most important steps in radiation
therapy of hepatocellular carcinoma. The segmentation task is often done manually, making …
therapy of hepatocellular carcinoma. The segmentation task is often done manually, making …
Medical image analysis using convolutional neural networks: a review
The science of solving clinical problems by analyzing images generated in clinical practice
is known as medical image analysis. The aim is to extract information in an affective and …
is known as medical image analysis. The aim is to extract information in an affective and …
Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …