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Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module
Y Jiang, L Chen, H Zhang, X **ao - PloS one, 2019 - journals.plos.org
Although successful detection of malignant tumors from histopathological images largely
depends on the long-term experience of radiologists, experts sometimes disagree with their …
depends on the long-term experience of radiologists, experts sometimes disagree with their …
Classification of breast tumors based on histopathology images using deep features and ensemble of gradient boosting methods
Breast cancer is the most common cancer among women worldwide. Early-stage diagnosis
of this disease can significantly improve the efficiency of treatment. Computer-Aided …
of this disease can significantly improve the efficiency of treatment. Computer-Aided …
Multiple instance learning for histopathological breast cancer image classification
Histopathological images are the gold standard for breast cancer diagnosis. During
examination several dozens of them are acquired for a single patient. Conventional, image …
examination several dozens of them are acquired for a single patient. Conventional, image …
[HTML][HTML] An investigation of XGBoost-based algorithm for breast cancer classification
Breast cancer is one of the leading cancers affecting women around the world. The
Computer-Aided Diagnosis (CAD) system is a powerful tool to assist pathologists during the …
Computer-Aided Diagnosis (CAD) system is a powerful tool to assist pathologists during the …
Classification of breast cancer based on histology images using convolutional neural networks
D Bardou, K Zhang, SM Ahmad - Ieee Access, 2018 - ieeexplore.ieee.org
In recent years, the classification of breast cancer has been the topic of interest in the field of
Healthcare informatics, because it is the second main cause of cancer-related deaths in …
Healthcare informatics, because it is the second main cause of cancer-related deaths in …
Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network
Abstract The Deep Convolutional Neural Network (DCNN) is one of the most powerful and
successful deep learning approaches. DCNNs have already provided superior performance …
successful deep learning approaches. DCNNs have already provided superior performance …
Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been
applied to many areas in different domains such as health care and drug design. Cancer …
applied to many areas in different domains such as health care and drug design. Cancer …
Residual learning based CNN for breast cancer histopathological image classification
Biopsy is one of the most commonly used modality to identify breast cancer in women,
where tissue is removed and studied by the pathologist under the microscope to look for …
where tissue is removed and studied by the pathologist under the microscope to look for …