Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
CAD and AI for breast cancer—recent development and challenges
Computer-aided diagnosis (CAD) has been a popular area of research and development in
the past few decades. In CAD, machine learning methods and multidisciplinary knowledge …
the past few decades. In CAD, machine learning methods and multidisciplinary knowledge …
Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images
Tumor classification and segmentation are two important tasks for computer-aided diagnosis
(CAD) using 3D automated breast ultrasound (ABUS) images. However, they are …
(CAD) using 3D automated breast ultrasound (ABUS) images. However, they are …
Differential deep convolutional neural network model for brain tumor classification
I Abd El Kader, G Xu, Z Shuai, S Saminu, I Javaid… - Brain Sciences, 2021 - mdpi.com
The classification of brain tumors is a difficult task in the field of medical image analysis.
Improving algorithms and machine learning technology helps radiologists to easily diagnose …
Improving algorithms and machine learning technology helps radiologists to easily diagnose …
C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation
Background and objective Breast lesions segmentation is an important step of computer-
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Dense prediction and local fusion of superpixels: A framework for breast anatomy segmentation in ultrasound image with scarce data
Segmentation of the breast ultrasound (BUS) image is an important step for subsequent
assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have …
assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have …
TBConvL-Net: A hybrid deep learning architecture for robust medical image segmentation
Deep learning has shown great potential for automated medical image segmentation to
improve the precision and speed of disease diagnostics. However, the task presents …
improve the precision and speed of disease diagnostics. However, the task presents …
Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints
Breast tumor segmentation is an important step in the diagnostic procedure of physicians
and computer-aided diagnosis systems. We propose a two-step deep learning framework for …
and computer-aided diagnosis systems. We propose a two-step deep learning framework for …
WGAN-based synthetic minority over-sampling technique: Improving semantic fine-grained classification for lung nodules in CT images
Data imbalance issue generally exists in most medical image analysis problems and maybe
getting important with the popularization of data-hungry deep learning paradigms. We …
getting important with the popularization of data-hungry deep learning paradigms. We …