A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
survival-prediction 开源项目推荐(88 个)
survival-prediction 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 88 个仓库Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021
使用最先进的卷积神经网络架构,包括3D UNet、3D VNet和2D UNet进行脑肿瘤分割,并使用分割的图像特征通过深度神经网络进行患者生存预测。
Attention-based Deep MIL implementation and application
A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
DLBCL-Morph dataset containing high resolution tissue microarray scans from 209 DLBCL cases, with geometric features computed using deep learning
DeepMTS: Deep Multi-Task Survival model for joint survival prediction and tumor segmentation
[ECCV 2026] A Patient-First Foundation Model for Computational Pathology
Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis
Code for the paper "Deep Cox Mixtures for Survival Regression", Machine Learning for Healthcare Conference 2021
Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data
Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models
ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction
Survival Prediction for Gastric Cancer via Multimodal Learning of Whole Slide Images and Gene Expression -- BIBM 2022
GraphLSurv: A Scalable Survival Prediction Network with Adaptive and Sparse Structure Learning for Histopathological Whole-Slide Images -- CMPB 2023
[MICCAI2023] XSurv: Merging-Diverging Hybrid Transformer Networks for Survival Prediction
Multi-modal approach for tumor segmentation and survival prediction using PET/CT imaging with attention mechanisms (MICCAI2021 HECKTOR Challenge).
Deep-learning (U-NET) CNN model for fully automatic glioma segmentation in 3D volume of MRI data, Survival prediction using different algorithms
We provide a method to extract the tractographic features from structural MR images for patients with brain tumor
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/survival-prediction。
- 该话题在 GitHub 上共有 88 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 1241 Star,覆盖 2 种主要语言,数据更新于 2026-11-10。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。