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
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities
Stefano Giacomelli, Marco Giordano, Claudia Rinaldi
The OCON model: an old but gold solution for distributable supervised classification
Stefano Giacomelli, Marco Giordano, Claudia Rinaldi
Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
Kelsey Lieberman, Swarna Kamlam Ravindran, Shuai Yuan, Carlo Tomasi
On Unsupervised Prompt Learning for Classification with Black-box Language Models
Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama
Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging
Evan Schwab, Bharat Annaldas, Nisha Ramesh, Anna Lundberg, Vishal Shelke, Xinran Xu, Cole Gilbertson, Jiyun Byun, Ernest T. Lam
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, Nagendra Kumar
Text Clustering as Classification with LLMs
Chen Huang, Guoxiu He
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language
Vincent Beliveau, Helene Kaas, Martin Prener, Claes N. Ladefoged, Desmond Elliott, Gitte M. Knudsen, Lars H. Pinborg, Melanie Ganz
Whole-Graph Representation Learning For the Classification of Signed Networks
Noé Cecillon (LIA), Vincent Labatut (LIA), Richard Dufour (LS2N - équipe TALN), Nejat Arınık (CRIL)
Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers
Tong Yao, Shreyas Sundaram
An Integrated Deep Learning Framework for Effective Brain Tumor Localization, Segmentation, and Classification from Magnetic Resonance Images
Pandiyaraju V, Shravan Venkatraman, Abeshek A, Aravintakshan S A, Pavan Kumar S, Madhan S
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
Amin Malekmohammadi, Ali Badiezadeh, Seyed Mostafa Mirhassani, Parisa Gifani, Majid Vafaeezadeh
SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss
Mengwen Ye, Yingzi Huangfu, You Li, Zekuan Yu