A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Artificial intelligence assists precision medicine in cancer treatment
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the
same drugs or surgical methods in patients with the same tumor may have different curative …
same drugs or surgical methods in patients with the same tumor may have different curative …
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 for magnification independent breast cancer histopathology image classification
N Bayramoglu, J Kannala… - 2016 23rd International …, 2016 - ieeexplore.ieee.org
Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer
which is the most common cancer among women. Pathology examination requires time …
which is the most common cancer among women. Pathology examination requires time …
From BoW to CNN: Two decades of texture representation for texture classification
Texture is a fundamental characteristic of many types of images, and texture representation
is one of the essential and challenging problems in computer vision and pattern recognition …
is one of the essential and challenging problems in computer vision and pattern recognition …
[HTML][HTML] Breast cancer histology images classification: Training from scratch or transfer learning?
R Mehra - Ict Express, 2018 - Elsevier
We demonstrated the ability of transfer learning in comparison with the fully-trained network
on the histopathological imaging modality by considering three pre-trained networks …
on the histopathological imaging modality by considering three pre-trained networks …
Deep learning for bone marrow cell detection and classification on whole-slide images
Bone marrow (BM) examination is an essential step in both diagnosing and managing
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …
Recent advances of deep learning for computational histopathology: principles and applications
Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy …
Background With the remarkable increasing in the numbers of patients, the triaging and
prioritizing patients into multi-emergency level is required to accommodate all the patients …
prioritizing patients into multi-emergency level is required to accommodate all the patients …
[HTML][HTML] A cluster-then-label semi-supervised learning approach for pathology image classification
Completely labeled pathology datasets are often challenging and time-consuming to obtain.
Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points …
Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points …