AI Medical Compendium Topic:
Diagnosis, Computer-Assisted

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DCANet: Dual contextual affinity network for mass segmentation in whole mammograms.

Medical physics
PURPOSE: Breast mass segmentation in mammograms remains a crucial yet challenging topic in computer-aided diagnosis systems. Existing algorithms mainly used mass-centered patches to achieve mass segmentation, which is time-consuming and unstable in c...

Artificial intelligence-the future is now.

Journal of assisted reproduction and genetics
The pros and cons of artificial intelligence in assisted reproductive technology are presented.

A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This s...

Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.

Scientific reports
Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultim...

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.

Scientific reports
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms...

A stacked ensemble for the detection of COVID-19 with high recall and accuracy.

Computers in biology and medicine
The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having...

Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers.

Computational and mathematical methods in medicine
Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate of lung cancer patients. Through computer technology, a comprehensive evaluation of genetic testing r...

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

PloS one
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most...

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Computational and mathematical methods in medicine
Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary c...

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery.

BMC nephrology
BACKGROUND: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible ...