AIMC Topic: Supervised Machine Learning

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Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison.

Computers in biology and medicine
Machine learning and data mining-based approaches to prediction and detection of heart disease would be of great clinical utility, but are highly challenging to develop. In most countries there is a lack of cardiovascular expertise and a significant ...

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

International journal of molecular sciences
In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays a...

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.

Medical image analysis
Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for various tasks, particularly for image classification. However, there are domains where the training of classification models that generalize on several datasets ...

Lidar-Camera Semi-Supervised Learning for Semantic Segmentation.

Sensors (Basel, Switzerland)
In this work, we investigated two issues: (1) How the fusion of lidar and camera data can improve semantic segmentation performance compared with the individual sensor modalities in a supervised learning context; and (2) How fusion can also be levera...

Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester.

Sensors (Basel, Switzerland)
Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to ag...

Aggregation-and-Attention Network for brain tumor segmentation.

BMC medical imaging
BACKGROUND: Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted d...

Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition.

Medical image analysis
Surgical workflow recognition is a fundamental task in computer-assisted surgery and a key component of various applications in operating rooms. Existing deep learning models have achieved promising results for surgical workflow recognition, heavily ...

Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning.

Methods of information in medicine
OBJECTIVE:  This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data norm...

Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.

Frontiers in public health
South Africa (SA) has the highest incidence of colorectal cancer (CRC) in Sub-Saharan Africa (SSA). However, there is limited research on CRC recurrence and survival in SA. CRC recurrence and overall survival are highly variable across studies. Accu...

Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean.

Medical image analysis
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approaches leverage small labeled data...