V

Vit Small Patch8 224.lunit Dino

Developed by 1aurent
An image classification model based on the Vision Transformer (ViT), trained on 33 million histological sections using the DINO self-supervised learning method, suitable for pathological image classification tasks.
Downloads 167
Release Time : 11/26/2023

Model Overview

This is a Vision Transformer model specifically designed for pathological image classification, which can serve as a basic model for feature extraction and is suitable for the field of medical image analysis.

Model Features

Training on large-scale pathological data
Trained on 33 million histological sections from multiple pathological datasets
Self-supervised learning
Trained using the DINO self-supervised learning method without the need for labeled data
Feature extraction backbone
Can serve as a basic model for feature extraction and is suitable for downstream tasks

Model Capabilities

Pathological image feature extraction
Histological section classification
Medical image analysis

Use Cases

Medical diagnosis
Cancer tissue classification
Identify and classify different types of cancer tissue sections
Pathological image analysis
Extract pathological image features for auxiliary diagnosis
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase