Benchmarking Vision Encoders for Survival Analysis using Histopathological Images

Abstract Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells in the body but can be prevented and even cured when detected early. Advanced medical imaging has introduced Whole Slide Images (WSIs). When combined with deep learning techniques, it can be used to extract meaningful features. These features are useful for various tasks such as classification and segmentation. There have been numerous studies involving the use of WSIs for survival analysis. Hence, it is crucial to determine their effectiveness for specific use cases. In this paper, we compared three publicly available vision encoders-UNI, Phikon and ResNet18 which are trained on millions of histopathological images, to generate feature embedding for survival analysis. WSIs cannot be fed directly to a network due to their size. We have divided them into 256 × 256 pixels patches and used a vision encoder to get feature embeddings. These embeddings were passed into an aggregator function to get representation at the WSI level which was then passed to a Long Short Term Memory (LSTM) based risk prediction head for survival analysis. Using breast cancer data from The Cancer Genome Atlas Program (TCGA) and k-fold cross-validation, we demonstrated that transformer-based models are more effective in survival analysis and achieved better C-index on average than ResNet-based architecture. The code1 for this study will be made available. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: We have used whole slide images and clinical data of patients (id, survival time, censored info only) of breast cancer from TCGA (https://portal.gdc.cancer.gov/projects/TCGA-BRCA). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes aritahalder{at}gmail.com Data Availability All data produced are available online at a private repository which will be made public. link- https://github.com/AsadNizami/Survival-Analysis