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Found 6 papers, 1 papers with code
A Systematic Review of Low-Rank and Local Low-Rank Matrix Approximation in Big Data Medical Imaging
We note a significant shift towards a preference for LLRMA in the medical imaging field since 2015, demonstrating its potential and effectiveness in capturing complex structures in medical data compared to LRMA.
Learning Disentangled Audio Representations through Controlled Synthesis
This paper tackles the scarcity of benchmarking data in disentangled auditory representation learning.
Learning Disentangled Speech Representations
This benchmark dataset and framework address the gap in the rigorous evaluation of state-of-the-art disentangled speech representation learning methods.
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
This study provides an empirical analysis of Barlow Twins (BT), an SSL technique inspired by theories of redundancy reduction in human perception.
Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images
Deep learning shows promise for medical image analysis but lacks interpretability, hindering adoption in healthcare.
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification
In this research, we have curated a novel dataset and developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images.
