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 exploring algorithm-data relationships.
We invite researchers from universities, hospitals, and industry to discuss their cutting-edge technological advances in all areas of ML models of cancer imaging, including novel imaging techniques (e.g., fast imaging, reconstruction and denoising etc.), multi-modality, multi-scale, multifactorial data analysis and data harmonization, explainable and interpretable artificial intelligence, radiomics and radiogenomics models, machine learning, and artificial intelligence in general.
Potential topics include but are not limited to:
● Create and interpret ML models for single- or multi-modal imaging (MRI, CT, Ultrasound, PET, SPECT).
● Multi-modality medical image multitask learning
● Improve explainability in cross-domain image synthesis between various imaging modalities or sequences (e.g., from different MRI sequences, or MRI and CT, etc.)
● Medical image transfer learning for single- or multi-modality images
● Novel imaging technologies for cancer, e.g., fast imaging, reconstruction, and denoising
● Quantitative imaging biomarker extraction and analysis
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
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 exploring algorithm-data relationships.
We invite researchers from universities, hospitals, and industry to discuss their cutting-edge technological advances in all areas of ML models of cancer imaging, including novel imaging techniques (e.g., fast imaging, reconstruction and denoising etc.), multi-modality, multi-scale, multifactorial data analysis and data harmonization, explainable and interpretable artificial intelligence, radiomics and radiogenomics models, machine learning, and artificial intelligence in general.
Potential topics include but are not limited to:
● Create and interpret ML models for single- or multi-modal imaging (MRI, CT, Ultrasound, PET, SPECT).
● Multi-modality medical image multitask learning
● Improve explainability in cross-domain image synthesis between various imaging modalities or sequences (e.g., from different MRI sequences, or MRI and CT, etc.)
● Medical image transfer learning for single- or multi-modality images
● Novel imaging technologies for cancer, e.g., fast imaging, reconstruction, and denoising
● Quantitative imaging biomarker extraction and analysis
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.