AIMC Topic: Supervised Machine Learning

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Cross-sequence semi-supervised learning for multi-parametric MRI-based visual pathway delineation.

Physics in medicine and biology
Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has been identified as an important way to delineate VP. However, due to ...

Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology.

Scientific reports
High-precision pixel-level annotation has been a major bottleneck in computational pathology due to its time-consuming nature and reliance on expert knowledge. Semi-supervised learning (SSL) provides a promising approach to alleviate this challenge b...

JMM-TGT: Self-supervised 3D action recognition through joint motion masking and topology-guided transformer.

PloS one
In the field of 3D skeleton action recognition, research on self-supervised learning methods has primarily focused on spatio-temporal feature modeling. However, these methods rely heavily on modeling single motion features, which limits their ability...

SSMCE: A semi-supervised learning framework for myocardial segmentation in myocardial contrast echocardiography.

Biomedical physics & engineering express
Accurate myocardial segmentation in myocardial contrast echocardiography (MCE) images remains challenging due to the scarcity of publicly available labeled datasets and the pervasive presence of speckle noise.Currently, echocardiographers must manual...

Advancing animal behavior recognition with self-supervised pre-training on unlabeled data.

Scientific reports
Deep learning-based animal activity recognition (AAR) achieves promising performance but remains constrained by its reliance on large labeled datasets. While pre-training offers a viable path toward reducing annotation dependency, existing approaches...

scSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering.

mSystems
UNLABELLED: Single-cell RNA sequencing (scRNA-seq) technology enables researchers to explore heterogeneity of diverse cell types within complex tissues at the single-cell resolution. Cell annotation, as a crucial step in scRNA-seq data analysis, prov...

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.

Scientific reports
Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data ...

Organic geochemical evidence for life in Archean rocks identified by pyrolysis-GC-MS and supervised machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Throughout Earth's history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the na...

Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.

Translational psychiatry
Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder diagnosed and subtyped solely based on clinical traits, which are prone to subjective judgment and lack of reliability. Also, the clinical subtyping does...

Efficient Vision Transformers for Ophthalmic Images Classification: A Comparative Study of Supervised, Semi-Supervised, and Unsupervised Learning Approaches.

Journal of medical systems
This study explored the integration of supervised, semi-supervised, and unsupervised learning strategies to classify ophthalmic images under label-scarce conditions. Given the high cost of annotations in medical imaging, the goal was to improve diagn...