survival-prediction

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021

Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.

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

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

[ECCV 2026] A Patient-First Foundation Model for Computational Pathology

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