Interpretability of Machine Intelligence in Medical Image Computing

Chapter 1 Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging

Chapter 2 Do Pre-processing and Augmentation Help Explainability? A Multi-seed Analysis for Brain Age Estimation

Chapter 3 Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data

Chapter 4 Reducing Annotation Need in Self-explanatory Models for Lung Nodule Diagnosis

Chapter 5 Attention-Based Interpretable Regression of Gene Expression in Histology

Chapter 7 Interpretable Vertebral Fracture Diagnosis

Chapter 8 Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT

Chapter 9 KAM -- a Kernel Attention Module for Emotion Classification with EEG Data

Chapter 10 Explainable Artificial Intelligence for Breast Tumour Classification: Helpful or Harmful