A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
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 …
Deep learning models in medical image analysis
M Tsuneki - Journal of Oral Biosciences, 2022 - Elsevier
Background Deep learning is a state-of-the-art technology that has rapidly become the
method of choice for medical image analysis. Its fast and robust object detection …
method of choice for medical image analysis. Its fast and robust object detection …
Clinically applicable deep learning for diagnosis and referral in retinal disease
The volume and complexity of diagnostic imaging is increasing at a pace faster than the
availability of human expertise to interpret it. Artificial intelligence has shown great promise …
availability of human expertise to interpret it. Artificial intelligence has shown great promise …
Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture
Histopathological image recognition of breast cancer is an onerous task. Although many
deep learning models have achieved good classification results on histopathological image …
deep learning models have achieved good classification results on histopathological image …
Deep learning in medical image analysis
Deep learning is the state-of-the-art machine learning approach. The success of deep
learning in many pattern recognition applications has brought excitement and high …
learning in many pattern recognition applications has brought excitement and high …
Review of deep learning approaches for thyroid cancer diagnosis
S Anari, N Tataei Sarshar, N Mahjoori… - Mathematical …, 2022 - Wiley Online Library
Thyroid nodule is one of the common life‐threatening diseases, and it had an increasing
trend over the last years. Ultrasound imaging is a commonly used diagnostic method for …
trend over the last years. Ultrasound imaging is a commonly used diagnostic method for …
Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods
Abstract The Global Cancer Statistics 2020 reported breast cancer (BC) as the most
common diagnosis of cancer type. Therefore, early detection of such type of cancer would …
common diagnosis of cancer type. Therefore, early detection of such type of cancer would …
Adversarial attack and defense technologies in natural language processing: A survey
S Qiu, Q Liu, S Zhou, W Huang - Neurocomputing, 2022 - Elsevier
Recently, the adversarial attack and defense technology has made remarkable
achievements and has been widely applied in the computer vision field, promoting its rapid …
achievements and has been widely applied in the computer vision field, promoting its rapid …
PatchResNet: multiple patch division–based deep feature fusion framework for brain tumor classification using MRI images
Modern computer vision algorithms are based on convolutional neural networks (CNNs),
and both end-to-end learning and transfer learning modes have been used with CNN for …
and both end-to-end learning and transfer learning modes have been used with CNN for …
AAPM task group report 273: recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep
learning (DL) techniques, have enabled broad application of these methods in health care …
learning (DL) techniques, have enabled broad application of these methods in health care …