AIMC Topic: Machine Learning

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Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

Journal of biophotonics
The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired...

A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Neural networks : the official journal of the International Neural Network Society
Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things (IoT) and Brain-Computer Interface (BCI) applications. From a signal...

Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).

Journal of medical systems
Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the br...

Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.

Biological psychiatry. Cognitive neuroscience and neuroimaging
Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automate...

Improving Generalization via Attribute Selection on Out-of-the-Box Data.

Neural computation
Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes) by sharing information of attributes between different objects. Attributes are artificially annotated for objects and treated eq...

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.

Nutrients
The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrie...

Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning.

Neural plasticity
According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphologi...

A validation of machine learning-based risk scores in the prehospital setting.

PloS one
BACKGROUND: The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a ma...

An investigation of machine learning methods in delta-radiomics feature analysis.

PloS one
PURPOSE: This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the...