# Medical image analysis

Lingshu is a SOTA multimodal large language model in the medical field, performing excellently in medical visual Q&A and report generation tasks.

MedGemma-4B-IT-4bit is a vision-language model specifically designed for the medical field, supporting image and text processing, and suitable for tasks such as medical image analysis.

This model is a fine-tuned version of the BLIP base model specifically for pathological slide images, used to automatically generate descriptive text for pathological slides.

An open-source image segmentation model under the Apache-2.0 license, suitable for various image segmentation tasks.

PyTorch-based Unet image segmentation model supporting various encoder architectures and pre-trained weights

A PyTorch-based semantic segmentation model that supports various encoder architectures, suitable for image segmentation tasks.

A PyTorch-based FPN image segmentation model supporting various encoder architectures, suitable for semantic segmentation tasks.

H0-mini is a lightweight histology foundation model jointly developed by Owkin and Bioptimus, based on the Vision Transformer architecture, trained via self-supervised distillation, and suitable for pathological image analysis.

An image segmentation model fine-tuned based on nvidia/mit-b0, trained on the NICOPOI-9/morphpad_hgo_512_4class dataset, excelling in high-precision image segmentation tasks.

PyTorch-based semantic segmentation model supporting multiple encoder architectures

A brain MRI image classification model based on ResNet18 architecture for cancer detection

CheXagent is a foundational model for chest X-ray interpretation, which can assist the medical field in professionally interpreting chest X-ray images.

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.

An X-ray image detection model fine-tuned based on AI-image-detector, achieving 99.83% accuracy

A model fine-tuned on blood cell object detection dataset based on DETA-ResNet-50 architecture, suitable for medical image analysis

Phikon is a self-supervised learning model for histopathology based on iBOT training, primarily used for extracting features from histology image patches.

A deep learning model for image segmentation tasks, capable of precisely segmenting different objects or regions in an image.

An image segmentation model fine-tuned on the tontokoton/artery-ultrasound-siit dataset based on nvidia/mit-b0

Image segmentation model fine-tuned on the Isaacks/ihc_slide_tissue dataset based on nvidia/mit-b0 model

Fine-tuned based on Google's ViT model for the diabetic retinopathy classification task, with an accuracy of 74.31%.

Cell segmentation model developed by Sribd-med team, suitable for cell instance segmentation tasks in multimodal images

This is a binary classification visual model built through an automated training process, excelling in medical image classification tasks.

This is a multi-class image classification model trained via AutoTrain, specifically designed for histopathology image classification tasks.

A binary image classification model based on the Google Vision Transformer (ViT) architecture, fine-tuned on a specific dataset to achieve high-precision classification

An image classification model fine-tuned for binary classification tasks based on facebook/convnext-small-224, achieving an F1 score of 0.9620 on the evaluation set

This model is a fine-tuned version based on Google's ViT (Vision Transformer) architecture on an unknown dataset, potentially for lung-related image analysis tasks.

An image classification model based on the ViT architecture, trained for 8 epochs on a specific dataset, achieving an accuracy of 94.99%.

fMRI image classification model based on Vision Transformer architecture, automatically generated by HuggingPics