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

July 19, 2023

July 18, 2023

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
Lars Nieradzik, Jördis Sieburg-Rockel, Stephanie Helmling, Janis Keuper, Thomas Weibel, Andrea Olbrich, Henrike Stephani
Deep Learning Classification Code Large Scale Tree Specie Image Data Microscopic Image Wood Specie

July 17, 2023

July 13, 2023

July 10, 2023

July 7, 2023

Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation
Dahyun Kang, Piotr Koniusz, Minsu Cho, Naila Murray
Vision Transformer Classification Code Segmentation Based Approach Self Supervised Vision Transformer Pixel Level Pseudo Label

July 2, 2023

A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification
Haitao Gan, Ran Zhou, Yanghan Ou, Furong Wang, Xinyao Cheng, Aaron Fenster
Classification Code Segmentation Task Region Specific Category Shift Carotid Atherosclerosis Carotid Plaque

June 29, 2023

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
Tao Wang, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Tao Tan, Min Du, Qinquan Gao, Tong Tong
Classification Code Active Learning Medical Image Segmentation Medical Image Classification Medical Image Datasets PCa Detection Small Perturbation Based Active Learning

June 28, 2023

Improving Primate Sounds Classification using Binary Presorting for Deep Learning
Michael Kölle, Steffen Illium, Maximilian Zorn, Jonas Nüßlein, Patrick Suchostawski, Claudia Linnhoff-Popien
Deep Learning Convolutional Neural Network Classification Code Audio Recording Multi Class Classification Task Binary Optimization MEL Spectrogram

June 27, 2023

June 25, 2023

DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
Hyun-Jic Oh, Won-Ki Jeong
Classification Code Nucleus Segmentation Data Synthesis Pathology Datasets Class Imbalanced Nucleus Sampling Nucleus Classification

Classification Code

Papers

Repeated Observations for Classification

Mechanical Artifacts in Optical Projection Tomography: Classification and Automatic Calibration

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning

Classification with Incoherent Kernel Dictionary Learning

A Quantum Convolutional Neural Network Approach for Object Detection and Classification

LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions

YOLIC: An Efficient Method for Object Localization and Classification on Edge Devices

Transformer-based end-to-end classification of variable-length volumetric data

Weakly-supervised positional contrastive learning: application to cirrhosis classification

SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings

Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

Robust Uncertainty Estimation for Classification of Maritime Objects

Internet of Things Fault Detection and Classification via Multitask Learning

Classification of sleep stages from EEG, EOG and EMG signals by SSNet

A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

Improving Primate Sounds Classification using Binary Presorting for Deep Learning

Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets