Attention-based Deep MIL implementation and application
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Updated
Jan 7, 2022 - Python
Attention-based Deep MIL implementation and application
Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021
Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data
A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data
DeepMTS: Deep Multi-Task Survival model for joint survival prediction and tumor segmentation
Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models
Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis
Gene Expression based Survival Prediction for Cancer Patients – A Topic Modeling Approach
[MICCAI2023] XSurv: Merging-Diverging Hybrid Transformer Networks for Survival Prediction
We provide a method to extract the tractographic features from structural MR images for patients with brain tumor
Code of the paper "Evolutionary convolutional neural network for efficient brain tumor segmentation and overall survival prediction
Repository containing reinforcement learning experiments for SMART-ACT project using the QuBBD data
[ACM-BCB 2023 Oral Presentation] Official repository of "Deep learning-based survival prediction using DNA methylation-derived 3D genomic information"
Survival Prediction for Gastric Cancer via Multimodal Learning of Whole Slide Images and Gene Expression -- BIBM 2022
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction
A fully-automatic end-to-end outcome prediction platform for oropharyngeal cancer
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