Multiple instance learning for histopathological breast cancer image classification
PJ Sudharshan, C Petitjean, F Spanhol… - Expert Systems with …, 2019 - Elsevier
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 …
Breast cancer histopathological image classification using convolutional neural networks
The performance of most conventional classification systems relies on appropriate data
representation and much of the efforts are dedicated to feature engineering, a difficult and …
representation and much of the efforts are dedicated to feature engineering, a difficult and …
A dataset for breast cancer histopathological image classification
Today, medical image analysis papers require solid experiments to prove the usefulness of
proposed methods. However, experiments are often performed on data selected by the …
proposed methods. However, experiments are often performed on data selected by the …
Deep features for breast cancer histopathological image classification
Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing
automated malignant BC detection system applied on patient's imagery can help dealing …
automated malignant BC detection system applied on patient's imagery can help dealing …
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 …
Mahotas: Open source software for scriptable computer vision
LP Coelho - arXiv preprint arXiv:1211.4907, 2012 - arxiv.org
Mahotas is a computer vision library for Python. It contains traditional image processing
functionality such as filtering and morphological operations as well as more modern …
functionality such as filtering and morphological operations as well as more modern …
Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks
R Man, P Yang, B Xu - IEEE access, 2020 - ieeexplore.ieee.org
Computer-aided diagnosis (CAD) systems of breast cancer histopathological images
automated classification can help reduce the manual observation workload of pathologists …
automated classification can help reduce the manual observation workload of pathologists …
Combining literature text mining with microarray data: advances for system biology modeling
A Faro, D Giordano, C Spampinato - Briefings in bioinformatics, 2012 - academic.oup.com
A huge amount of important biomedical information is hidden in the bulk of research articles
in biomedical fields. At the same time, the publication of databases of biological information …
in biomedical fields. At the same time, the publication of databases of biological information …
Pre‐trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology
Several researchers are trying to develop different computer‐aided diagnosis system for
breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms …
breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms …
[HTML][HTML] Grading of invasive breast carcinoma through Grassmannian VLAD encoding
In this paper we address the problem of automated grading of invasive breast carcinoma
through the encoding of histological images as VLAD (Vector of Locally Aggregated …
through the encoding of histological images as VLAD (Vector of Locally Aggregated …