Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024

读过的CV方向的一些论文，图像生成文字、弱监督分割等

[MICCAI 2021] Boundary-aware Transformers for Skin Lesion Segmentation

[MICCAI'21] [Tensorflow] Retinal Vessel Segmentation using a Novel Multi-scale Generative Adversarial Network

[MedIA] Paper list and source code for survey "A comprehensive survey on deep active learning in medical image analysis"

Awesome Active Domain Adaptation for Medical Image Analysis

[MICCAI 2020] BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture

MICCAI 2023: Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

Repository containing the source code of the IVD-Net segmentation network that we proposed for the MICCAI 2018 IVD segmentation challenge.

AIOZ AI - Overcoming Data Limitation in Medical Visual Question Answering (MICCAI 2019)

MICCAI 2022: Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

[MICCAI'21 & MICCAI'20] A Codebase for Universal Lesion Detection (DeepLesion SOTA)

Rank3 Code for ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, Task 3

Code for Paper: Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response, in MICCAI2019.

[MICCAI'2024, Oral] Official implementation of BrLP method from "Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge"

[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation

[MICCAI 2021] BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation

[MICCAI 2024] CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

Official Pytorch implementation of MICCAI 2024 paper (early accept, top 11%) Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography