AIMC Topic: Machine Learning

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Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.

Neural networks : the official journal of the International Neural Network Society
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, ti...

Semantic Mask Reconstruction and Category Semantic Learning for few-shot image generation.

Neural networks : the official journal of the International Neural Network Society
Few-shot image generation aims at generating novel images for the unseen category when given K images from the same category. Despite significant advancements in existing few-shot image generation methods, great challenges remain regarding the qualit...

A Machine learning classification framework using fused fractal property feature vectors for Alzheimer's disease diagnosis.

Brain research
Alzheimer's disease (AD) profoundly affects brain tissue and network structures. Analyzing the topological properties of these networks helps to understand the progression of the disease. Most studies focus on single-scale brain networks, but few add...

Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers.

Ultrasound in medicine & biology
OBJECTIVE: Early and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic f...

Machine learning in predicting cauda equina imaging outcomes- a solution to the problem.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Cauda Equina Syndrome (CES) is a rare surgical emergency. The implications for loss of quality of life through delayed management are high, though no clinical symptom is pathognomonic in its diagnosis. We describe how machine learning based ...

Real-time 3D MR guided radiation therapy through orthogonal MR imaging and manifold learning.

Medical physics
BACKGROUND: In magnetic resonance image (MRI)-guided radiotherapy (MRgRT), 2D rapid imaging is commonly used to track moving targets with high temporal frequency to minimize gating latency. However, anatomical motion is not constrained to 2D, and a p...

Enhancing resource recovery from acid whey through chitosan-based pretreatment and machine learning optimization.

Bioresource technology
Acid whey, a dairy byproduct with low pH and high organic content, presents disposal challenges but also potential for resource recovery. In this study, chitosan gel was synthesized and evaluated for turbidity reduction of acid whey. Machine learning...

Predictive analysis of COVID-19 occurrence and vaccination impacts across the 50 US states.

Computers in biology and medicine
OBJECTIVE: This study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities. The proposed model was evaluated using external validation to ensure optimal protection of vaccinat...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

British journal of haematology
Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytogenetics and molecular biology) provides comprehensive diagnostic understandings. However, MBN subtypi...