Medical Image Classification

Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Deep Residual Learning for Image Recognition

Content

Densely Connected Convolutional Networks

RegNet: Self-Regulated Network for Image Classification

Res2Net: A New Multi-Scale Backbone Architecture

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

ResNet strikes back: An improved training procedure in timm

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

DaViT: Dual Attention Vision Transformers

Contrastive Learning of Medical Visual Representations from Paired Images and Text