Multi-stage Multimodal Progressive Learning for Coordinated Segmentation, Diagnosis, and Prognosis in Head and Neck Cancer

Yue Lin, Shenghai Wu, Hao Wang, Lei Bi, Mingyuan Meng

Keywords: Multi-stage Progressive Learning, Multimodal Learning, Segmentation, Diagnosis, Prognosis

Abstract: Head and Neck (H&N) cancer is among the most common cancers worldwide, and its related clinical decision-making constitutes a systematic process that requires the integration of multimodal clinical data and the coordination of diverse tasks in the clinical workflow. However, how to effectively coordinate the interrelated clinical tasks to maximize their synergistic potential is still an open question. In this study, we propose a Multi-stage Multimodal Progressive Learning (named MMPL) framework for coordinated modeling of segmentation, diagnosis, and prognosis tasks, in the context of HECKTOR 2025 challenge at MICCAI 2025. Our MMPL progressively learns three clinical tasks that collectively facilitate personalized treatment planning: (i) tumor segmentation, (ii) HPV status classification, and (iii) survival prediction. Specifically, we establish a unified network backbone, consisting of a triple-stream encoder with adaptive PET/CT information fusion and an attention-gated decoder that can be applied to all three tasks. This backbone is successively trained for segmentation, classification, and survival prediction at three learning stages, where the knowledge is progressively learned with the guidance of prior knowledge accumulating from former stages. Further, the intermediate outputs (e.g., segmentation masks, HPV status) are leveraged as guidance on radiomics analysis or as supplementary indicators for the final prediction. Our team (InterStellar) attained top-tier performance across all three tasks in the validation phase, while the final testing results have yet to be released.

Submission Number: 13