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Segmentation of head and neck (H\&N) tumours and prediction of patientoutcome are crucial for patient's disease diagnosis and treatment monitoring.Current developments of robust deep learning models are hindered by the lack oflarge multi-centre, multi-modal data with quality annotations. The MICCAI 2021HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challengecreates a platform for comparing segmentation methods of the primary grosstarget volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography imagesand prediction of progression-free survival in H\&N oropharyngeal cancer.Forthe segmentation task, we proposed a new network based on an encoder-decoderarchitecture with full inter- and intra-skip connections to take advantage oflow-level and high-level semantics at full scales. Additionally, we usedConditional Random Fields as a post-processing step to refine the predictedsegmentation maps. We trained multiple neural networks for tumor volumesegmentation, and these segmentations were ensembled achieving an average DiceSimilarity Coefficient of 0.75 in cross-validation, and 0.76 on the challengetesting data set. For prediction of patient progression free survival task, wepropose a Cox proportional hazard regression combining clinical, radiomic, anddeep learning features. Our survival prediction model achieved a concordanceindex of 0.82 in cross-validation, and 0.62 on the challenge testing data set.

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