AIMC Topic:
Supervised Machine Learning
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Efficient deep learning model for mitosis detection using breast histopathology images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 16, 2017
Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer grading. It provides vital clues to estimate the aggressiveness and the proliferation rate of the tumour. The...
Optimal projection method determination by Logdet Divergence and perturbed von-Neumann Divergence.
BMC systems biology
Dec 14, 2017
BACKGROUND: Positive semi-definiteness is a critical property in kernel methods for Support Vector Machine (SVM) by which efficient solutions can be guaranteed through convex quadratic programming. However, a lot of similarity functions in applicatio...
Classification of cancer cells using computational analysis of dynamic morphology.
Computer methods and programs in biomedicine
Dec 7, 2017
BACKGROUND AND OBJECTIVE: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cance...
Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.
Addictive behaviors
Nov 27, 2017
BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical.
Glioma Survival Prediction with Combined Analysis of In Vivo C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Nov 24, 2017
Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done using MRI. Nevertheles...
Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.
Comparative biochemistry and physiology. Part D, Genomics & proteomics
Nov 11, 2017
In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present ...
Contaminant source identification using semi-supervised machine learning.
Journal of contaminant hydrology
Nov 8, 2017
Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sou...
SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.
Artificial intelligence in medicine
Oct 27, 2017
With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., dr...
Classification of the trabecular bone structure of osteoporotic patients using machine vision.
Computers in biology and medicine
Oct 13, 2017
Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done usin...
A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.
Scientific reports
Oct 12, 2017
Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignor...
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