AIMC Topic: Supervised Machine Learning

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Artificial intelligence strategies based on random forests for detection of AI-generated content in public health.

Public health
OBJECTIVES: To train and test a Random Forest machine learning model with the ability to distinguish AI-generated from human-generated textual content in the domain of public health, and public health policy.

Boosting semi-supervised federated learning by effectively exploiting server-side knowledge and client-side unconfident samples.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised federated learning (SSFL) has emerged as a promising paradigm to reduce the need for fully labeled data in training federated learning (FL) models. This paper focuses on the label-at-server scenario, where clients' data are entirely u...

MedScale-Former: Self-guided multiscale transformer for medical image segmentation.

Medical image analysis
Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive label...

ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit an...

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

IEEE journal of biomedical and health informatics
The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pat...

Prior Visual-Guided Self-Supervised Learning Enables Color Vignetting Correction for High-Throughput Microscopic Imaging.

IEEE journal of biomedical and health informatics
Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent ...

Aceso-DSAL: Discovering Clinical Evidences From Medical Literature Based on Distant Supervision and Active Learning.

IEEE journal of biomedical and health informatics
Automatic extraction of valuable, structured evidence from the exponentially growing clinical trial literature can help physicians practice evidence-based medicine quickly and accurately. However, current research on evidence extraction has been limi...

Pyramid Pixel Context Adaption Network for Medical Image Classification With Supervised Contrastive Learning.

IEEE transactions on neural networks and learning systems
Spatial attention (SA) mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysi...

Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy.

PloS one
Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approac...

Self-Supervised Graph Representation Learning for Single-Cell Classification.

Interdisciplinary sciences, computational life sciences
Accurately identifying cell types in single-cell RNA sequencing data is critical for understanding cellular differentiation and pathological mechanisms in downstream analysis. As traditional biological approaches are laborious and time-intensive, it ...