Cancer Survival

Predicting cancer survival is crucial for personalized treatment planning and improving patient outcomes. Current research heavily emphasizes the development of sophisticated multimodal machine learning models, incorporating genomic data, pathology images, and patient history, often utilizing architectures like transformer networks, convolutional masked autoencoders, and heterogeneous graph networks to improve prediction accuracy and robustness. These advancements aim to overcome challenges like data imbalance and missing information, ultimately leading to more precise survival estimations and potentially informing the development of more effective cancer therapies. The improved accuracy and interpretability of these models hold significant promise for advancing clinical practice and improving patient care.

Papers

March 18, 2024

Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
Hongxiao Wang, Yang Yang, Zhuo Zhao, Pengfei Gu, Nishchal Sapkota, Danny Z. Chen
Survival Prediction Outcome Prediction Cancer Genome Atlas Cancer Survival Balanced Multimodal Learning

March 14, 2024

SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
Liangrui Pan, Yijun Peng, Yan Li, Xiang Wang, Wenjuan Liu, Liwen Xu, Qingchun Liang, Shaoliang Peng
Deep Convolutional Neural Network Supervised Autoencoder Multimodal Prediction Heterogeneous Graph Network Cancer Survival Multimodal Survival

September 25, 2023

Predicting environment effects on breast cancer by implementing machine learning
Muhammad Shoaib Farooq, Mehreen Ilyas
Machine Learning Breast Cancer Breast Cancer Risk Environmental Factor Cancer Survival

August 4, 2023

Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
William Gao, Dayong Wang, Yi Huang
Deep Learning Medical Diagnosis Diagnostic Model Significant Delay Change Breast Cancer Metastasis Cancer Survival

September 12, 2022

TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
Numan Saeed, Ikboljon Sobirov, Roba Al Majzoub, Mohammad Yaqub
End to End Segmentation Based Approach Multimodal Transformer Survival Prediction Multimodal Deep Learning Multimodal Network Neck Tumor Segmentation Cancer Survival

April 24, 2022

Colorectal cancer survival prediction using deep distribution based multiple-instance learning
Xingyu Li, Jitendra Jonnagaddala, Min Cen, Hong Zhang, Xu Steven Xu
Survival Prediction Lung Cancer Colorectal Cancer Instance Learning Multiple Instance Cancer Survival

Cancer Survival

Papers

Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction

SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival

Predicting environment effects on breast cancer by implementing machine learning

Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries

TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction

Colorectal cancer survival prediction using deep distribution based multiple-instance learning