Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …
learns a sequence of actions that maximizes the expected reward, with the representative …
A review on deep-learning algorithms for fetal ultrasound-image analysis
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US)
fetal images. A number of survey papers in the field is today available, but most of them are …
fetal images. A number of survey papers in the field is today available, but most of them are …
A survey on cancer detection via convolutional neural networks: Current challenges and future directions
Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
Unified medical image segmentation by learning from uncertainty in an end-to-end manner
Automatic segmentation is a fundamental task in computer-assisted medical image analysis.
Convolutional neural networks (CNNs) have been widely used for medical image …
Convolutional neural networks (CNNs) have been widely used for medical image …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Uncertainty aware temporal-ensembling model for semi-supervised abus mass segmentation
Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a
crucial role in 3D breast reconstruction which can assist radiologists in surgery planning …
crucial role in 3D breast reconstruction which can assist radiologists in surgery planning …
AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Mammography is one of the most commonly applied tools for early breast cancer screening.
Automatic segmentation of breast masses in mammograms is essential but challenging due …
Automatic segmentation of breast masses in mammograms is essential but challenging due …
Multi-task learning for quality assessment of fetal head ultrasound images
It is essential to measure anatomical parameters in prenatal ultrasound images for the
growth and development of the fetus, which is highly relied on obtaining a standard plane …
growth and development of the fetus, which is highly relied on obtaining a standard plane …
SESV: Accurate medical image segmentation by predicting and correcting errors
Medical image segmentation is an essential task in computer-aided diagnosis. Despite their
prevalence and success, deep convolutional neural networks (DCNNs) still need to be …
prevalence and success, deep convolutional neural networks (DCNNs) still need to be …