[HTML][HTML] Machine learning methods for histopathological image analysis
D Komura, S Ishikawa - Computational and structural biotechnology journal, 2018 - Elsevier
Abundant accumulation of digital histopathological images has led to the increased demand
for their analysis, such as computer-aided diagnosis using machine learning techniques …
for their analysis, such as computer-aided diagnosis using machine learning techniques …
Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review
Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …
common in men, with an increasing incidence. Pathology diagnosis complemented with …
DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system
Examination of pathological images is the golden standard for diagnosing and screening
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …
BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
There are several breast cancer datasets for building Computer Aided Diagnosis systems
(CADs) using either deep learning or traditional models. However, most of these datasets …
(CADs) using either deep learning or traditional models. However, most of these datasets …
Crccn-net: Automated framework for classification of colorectal tissue using histopathological images
Colorectal cancer has a high mortality rate that continuously affects human life globally.
Early detection of it extends human life and helps in preventing disease. Histopathological …
Early detection of it extends human life and helps in preventing disease. Histopathological …
Deep learning applied for histological diagnosis of breast cancer
Deep learning, as one of the currently most popular computer science research trends,
improves neural networks, which has more and deeper layers allowing higher abstraction …
improves neural networks, which has more and deeper layers allowing higher abstraction …
Colorectal histology tumor detection using ensemble deep neural network
S Ghosh, A Bandyopadhyay, S Sahay, R Ghosh… - … Applications of Artificial …, 2021 - Elsevier
With a mortality rate of approximately 33.33%, Colorectal cancer serves as the second most
prevalent malignant tumor type in the world. AI-guided clinical care/tool can help in reducing …
prevalent malignant tumor type in the world. AI-guided clinical care/tool can help in reducing …
Deep learning in histopathology: A review
Histopathology is diagnosis based on visual examination of tissue sections under a
microscope. With the growing number of digitally scanned tissue slide images, computer …
microscope. With the growing number of digitally scanned tissue slide images, computer …
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
A Rączkowska, M Możejko, J Zambonelli… - Scientific reports, 2019 - nature.com
Abstract Machine learning algorithms hold the promise to effectively automate the analysis
of histopathological images that are routinely generated in clinical practice. Any machine …
of histopathological images that are routinely generated in clinical practice. Any machine …
A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological …
A clinically comparable Convolutional Neural Network framework-based technique for
performing automated classification of cancer grades and tissue structures in hematoxylin …
performing automated classification of cancer grades and tissue structures in hematoxylin …