Advances and New Insights into Cancer Characterization: When Novel Imaging Meets Quantitative Imaging Biomarkers

Computational medical imaging approaches can improve the analytical accuracy of interpretation in cancer identification and characterization, allowing for earlier disease detection and a better understanding of physiology and pathology. Machine Learning (ML) models have revolutionized many activities of medical imaging applications, such as novel imaging techniques, segmentation, registration, and synthesis, by analyzing large amounts of quantitative imaging biomarkers. While ML models outperform traditional methods on these tasks, they are still largely tacit in terms of explaining the data under consideration.This has reduced the interpretability of ML models, which is one of the major obstacles to ML-based pathology identification and generalized single- or multi-modal and multi-scale interpretation in medical imaging. Detailed examples of model behaviors are expected in current clinical practices to promote reliability and improve clinical decision making. Furthermore, the primary challenge for designing explainable models is to provide rationales while retaining high learning results as one of the most exciting areas of medical imaging science.We hope to attract novel, high-quality research and survey papers that represent the most recent developments in ML models in innovative medical imaging (MRI, CT, PET, SPECT, Ultrasound, histology and others) modalities or multi-modalities, by investigating novel methodologies either by interpreting algorithm components or by ex...