Classification Code
Classification code research focuses on developing and improving algorithms and models to accurately assign data points to predefined categories. Current efforts concentrate on addressing challenges like imbalanced datasets, noisy data, and limited labeled data through techniques such as self-supervised pre-training, robust loss functions, and the application of diverse architectures including convolutional neural networks (CNNs), transformers, and novel approaches like Mamba. These advancements have significant implications across various fields, improving accuracy and efficiency in applications ranging from medical image analysis and bioacoustic monitoring to cybersecurity threat detection and scientific literature organization.
Papers
Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask
R. Chinnaiyan, Iyyappan M, Al Raiyan Shariff A, Kondaveeti Sai, Mallikarjunaiah B M, P Bharath
Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification
Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari, Luoyao Chen, Mei Chen, Jinqian Pan, Craig Smuda, Jacopo Cirrone
Classification of developmental and brain disorders via graph convolutional aggregation
Ibrahim Salim, A. Ben Hamza
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images
Aryan Singh, Pepijn Van de Ven, Ciarán Eising, Patrick Denny
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
Marc Aubreville, Zhaoya Pan, Matti Sievert, Jonas Ammeling, Jonathan Ganz, Nicolai Oetter, Florian Stelzle, Ann-Kathrin Frenken, Katharina Breininger, Miguel Goncalves