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Multi-Task Deep Learning for Head and Neck Cancer: Segmentation, Survival Prediction, and HPV Classification in the HECKTOR 2025 Challenge

MCML Authors

Jakob Dexl

→ Group Michael Ingrisch
Clinical Data Science in Radiology

Michael Ingrisch

Prof. Dr.

Principal Investigator

Clinical Data Science in Radiology

Abstract

This paper describes our submissions (team CDS) to the HECKTOR 2025 challenge, which addresses three tasks: (1) tumor and lymph node segmentation, (2) recurrence-free survival prediction, and (3) HPV status classification. For Task 1, we trained a baseline UNet and refined the final model using stochastic weight averaging and small lesion removal. For Task 2, we employed a lightweight 3D ResNet18 that combines PET, CT, segmentation masks, and clinical metadata, optimized with a Cox loss. For Task 3, we extended the segmenta- tion model with a classification head and metadata integration. Cross- validation results were promising, performance on the preliminary vali- dation set was however lower, underlining the challenges of generaliza- tion in multi-center cohorts.

inproceedings DI25

HECKTOR @MICCAI 2025

4th Head and Neck Cancer Tumor Lesion Segmentation, Diagnosis and Prognosis Challenge at the 28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025.