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Supervised Machine Learning

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Weakly Supervised Classification of Mohs Surgical Sections Using Artificial Intelligence.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Basal cell carcinoma (BCC) is the most frequently diagnosed form of skin cancer, and its incidence continues to rise, particularly among older individuals. This trend puts a significant strain on health care systems, especially in terms of histopatho...

A novel semi-supervised learning model based on pelvic radiographs for ankylosing spondylitis diagnosis reduces 90% of annotation cost.

Computers in biology and medicine
OBJECTIVE: Our study aims to develop a deep learning-based Ankylosing Spondylitis (AS) diagnostic model that achieves human expert-level performance using only a minimal amount of labeled samples for training, in regions with limited access to expert...

Enhancing mosquito classification through self-supervised learning.

Scientific reports
Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learnin...

Weakly Supervised Multiple Instance Learning Model With Generalization Ability for Clinical Adenocarcinoma Screening on Serous Cavity Effusion Pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Accurate and rapid screening of adenocarcinoma cells in serous cavity effusion is vital in diagnosing the stage of metastatic tumors and providing prompt medical treatment. However, it is often difficult for pathologists to screen serous cavity effus...

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.

Scientific reports
Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the establis...

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide ...

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

IEEE journal of biomedical and health informatics
Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenie...

SFWN: A Novel Semi-Supervised Feature Weighted Neural Network for Gene Data Feature Learning and Mining With Graph Modeling.

IEEE journal of biomedical and health informatics
Gene expression data can serve for analyzing the genes with changed expressions, the correlation between genes and the influence of different circumstance on gene activities. However, labeling a large number of gene expression data is laborious and t...

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Scientific reports
Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets hav...

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

IEEE transactions on medical imaging
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical ...