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Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
Code for the Evidential Conditional Neural Processes
【MICCAI 2023 Early Accept & MedIA submission】EyeMost "Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributions"
[NeurIPS 2022] GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
【ASOC 2022】"An interactive dual-branch network for hard palate segmentation of the oral cavity from CBCT images"
MedRG: Medical Report Grounding with Multi-modal Large Language Model
Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM
PraNet: Parallel Reverse Attention Network for Polyp Segmentation, MICCAI 2020 (Oral). Code using Jittor Framework is available.
【MICCAI 2023 REMIA workshop】 SAM-U
基于pytorch版ssd进行改进注入CBAM空间通道注意力机制,加入FPN,类别损失函数改为focalloss
【MICCAI 2022】"TBraTS: Trusted Brain Tumor Segmentation"
A simple way to calibrate your neural network.
The project page of paper: Trusted Multi-View Classification [ICLR'2021 paper]
Medical imaging toolkit for deep learning
This repo provides the official code for : 1) TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/abs/2103.04430) , accepted by MICCAI2021. 2) TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images(https://arxiv.org/abs/2201.12785).
A benchmark for Uncertainty quantification
【Nature Communications 2023】 Uncertainty-inspired open set learning for retinal anomaly identification
【MICCAI 2023】"Uncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation".
High-quality implementations of standard and SOTA methods on a variety of tasks.
This repository contains a collection of surveys for predictive uncertainty estimation in deep learning models.
A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation