MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images

2021-10-22 · Shuai Jiang, Arief A. Suriawinata, Saeed Hassanpour

In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest. However, given the large size of WSIs and the lack of pathologist annotations, extracting the prognostic information from WSIs remains a challenging task. Previous studies have used multiple instance learning approaches to combine the information from multiple randomly sampled patches, but different visual patterns may contribute differently to prognosis prediction. In this study, we developed a multi-head attention approach to focus on various parts of a tumor slide, for more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming two existing state-of-the-art approaches for WSI-based survival prediction, which have an average c-index of 0.603 and 0.619 on these datasets. Visualization of our attention maps reveals each attention head focuses synergistically on different morphological patterns.

📄 PDF Abstract BibTeX arXiv:2110.11558

Code (0)

등록된 구현이 없습니다.

Tasks

Multiple Instance Learning PredictionPrognosisSurvival Predictionwhole slide images

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

2023-07-07 · Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng 외

Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and medical images have shown promising perform…

Decoder Prediction RAG Survival Prediction+1

Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion

2024-10-29 · Aiman Farooq, Utkarsh Sharma, Deepak Mishra

Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely use…

Decision Making Prediction Survival Prediction

TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction

2023-11-13 · Ruiquan Ge, Xiangyang Hu, Rungen Huang, Gangyong Jia 외

Survival prediction plays a crucial role in assisting clinicians with the development of cancer treatment protocols. Recent evidence shows that multimodal data can help in the diagnosis of cancer disease and improve surv…

Prediction Survival Prediction

Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention

2021-11-06 · Emmanuelle Bourigault, Daniel R. McGowan, Abolfazl Mehranian, Bartłomiej W. Papież

Segmentation of head and neck (H\&N) tumours and prediction of patient outcome are crucial for patient's disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the…

Decoder Prediction Segmentation Survival Prediction

Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation

2024-05-09 · Meixu Chen, Kai Wang, Jing Wang

A comprehensive and reliable survival prediction model is of great importance to assist in the personalized management of Head and Neck Cancer (HNC) patients treated with curative Radiation Therapy (RT). In this work, we…

Decision Making Multi-Label Learning Prediction regression+1