Trustworthy Analysis of Radiotherapy Images with Artificial Intelligence

Abstract: Head and neck squamous cell carcinoma (HNSCC) requires timely, accurate detection, yet expert PET/CT interpretation is challenged by low-contrast lesions, heterogeneous presentation, and severe class imbalance. This thesis develops methods that jointly enhance detection performance, quantify predictive uncertainty, and deliver validated, slice-level localisation suitable for triage. 

First, we investigate Capsule Networks (CapsNets), systematically varying Primary Capsule dimensionality to study its effect on efficiency and accuracy. Results show capsule configuration is a critical, tunable hyperparameter rather than a fixed specification. 

Second, we evaluate CT-based classification. For slice-level tumour detection, convolutional and transformer baselines outperform CapsNets (CNN: AUROC 0.90, accuracy 0.832, sensitivity 0.961; ViT: AUROC 0.89, accuracy 0.676, sensitivity 0.987; CapsNet: AUROC 0.640, accuracy 0.506, sensitivity 0.914). For seven-class primary site classification, initially high scores (accuracy 0.8553, AUCs ≥0.97) were attributable to patient-level leakage, motivating strict patient-wise splitting and explainability checks. 

Third, we propose a multi-view PET classifier that fuses axial, coronal, and sagittal planes via cross-attention, exploiting complementary spatial context. Relative to single-view transformers, sensitivity improved by up to 0.40, reaching 0.94. Interpretability is supported by DeepLIFT with connected-component analysis (localisation sensitivity 0.41 at 1 false positive per slice), and calibrated uncertainty via test-time augmentation.

Collectively, these contributions demonstrate that multi-view fusion, coupled with rigorous evaluation and validated explainability, yields uncertainty-aware, interpretable models for HNSCC triage. Future work includes external, multi-institutional validation, extension to CT-only fusion, PET/CT multimodal integration, and assessment of generalisability to additional cancers.