CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Code release for NeRF (Neural Radiance Fields)

EoFormer: Edge-oriented Transformer for Brain Tumor Segmentation

A latent text-to-image diffusion model

Repository for 'Multimodal Image Registration Guided by Few Segmentations from One Modality'

Code Implementation of EfficientVMamba

Code for the ICML 2021 (long talk) paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

High-Resolution Image Synthesis with Latent Diffusion Models

A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting [ECCV 2024]

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeX…

[MICCAI 2022] The official code for "mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation"

Multiscale Multimodal Vison Transformer for Brain Tumor Segmentation with Missing Modalities

Dataset of medical images, captions, subfigure-subcaption annotations, and inline textual references

MICCAI 2019-2023 Open Source Papers

EchoNet-Dynamic is a deep learning model for assessing cardiac function in echocardiogram videos.

[TMI'20] Unpaired Multi-modal Segmentation via Knowledge Distillation

Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

backup some books

METER: A Multimodal End-to-end TransformER Framework

[MICCAI-2022] This is the official implementation of Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training.

This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

Pytorch implementation of SMIL: Multimodal Learning with Severely Missing Modality (AAAI 2021)

Radiology Objects in COntext (ROCO): A Multimodal Image Dataset

李宏毅《深度强化学习》笔记，在线阅读地址：https://datawhalechina.github.io/leedeeprl-notes/

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth