Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
[Retracted] U‐Net‐Based Medical Image Segmentation
Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
Segment anything model for medical image analysis: an experimental study
Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …
Segmenter: Transformer for semantic segmentation
Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …
requires contextual information to reach label consensus. In this paper we introduce …
[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 …
Transfuse: Fusing transformers and cnns for medical image segmentation
Medical image segmentation-the prerequisite of numerous clinical needs-has been
significantly prospered by recent advances in convolutional neural networks (CNNs) …
significantly prospered by recent advances in convolutional neural networks (CNNs) …
A review of deep-learning-based medical image segmentation methods
As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …
made great contributions to sustainable medical care. Now it has become an important …
Convolutional neural networks in medical image understanding: a survey
DR Sarvamangala, RV Kulkarni - Evolutionary intelligence, 2022 - Springer
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
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 …
Structural crack detection using deep convolutional neural networks
Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …
range of computer vision problems. It has achieved encouraging results in numerous …