Advances in medical image analysis have facilitated the development of processes for high-throughput extraction of quantitative features. It promotes converting images into a large amount of high-dimensional data and the subsequent analysis of these data for clinical decision-making support. For example, data across multiple scales and modalities in clinics, including radiology images and pathological images, were used to create diagnostic tools with better performance.
Machine learning and deep learning methods have been adapted to clinical challenges, including accurate computer-aided diagnoses, monitoring of drug efficacy, and prediction of treatment response or clinical outcomes. In addition, deep learning techniques have been proved helpful for auto-segmentation of targets and capture of features, which provide the possibility of maximum use of image data for clinical management.
Integrating quantitative measurements from radiology and pathology data for personalized medicine remains challenging in terms of robust data provenance, optimization, tuning parameters for machine learning and deep learning algorithms, and standard process procedures. Radiomics can successfully extract information from radiology images and establish image biomarkers for predictive and prognostic modeling. In contrast to radiomics, pathomics analyzes digital microscopy images of tissue, cells, and subcellular structures. Deep learning algorithms can help automatically extract proposed features or develop new features that are hard to explain.
Currently, a computational method for extracted features should consider the discrimination ability and need to consider explainability. Therefore, a deep learning model, treated as a “black box,” should be used for the isolated process instead of all procedures.
This topic aims to focus on applying data mining in radiology and pathology images using deep learning or machine learning techniques. We welcome the submission of original research and review articles that include image analysis in clinical studies and applications and technologies or discoveries in computational approaches.
• Computational approaches to analyzing images for noninvasive diagnosis of disease.
• Computational approaches to analyzing images for predicting survival outcomes.
• Computational approaches to analyzing images for predicting treatment response.
• Deep learning-based analysis of radiological-pathological images for biomarkers.
• Comparisons of different computational approaches for the same purpose in medicine.
• Correlations between radiomics, pathomics, and genomics.
• Integration of radiomics and pathomics for the establishment of prediction models.
Advances in medical image analysis have facilitated the development of processes for high-throughput extraction of quantitative features. It promotes converting images into a large amount of high-dimensional data and the subsequent analysis of these data for clinical decision-making support. For example, data across multiple scales and modalities in clinics, including radiology images and pathological images, were used to create diagnostic tools with better performance.
Machine learning and deep learning methods have been adapted to clinical challenges, including accurate computer-aided diagnoses, monitoring of drug efficacy, and prediction of treatment response or clinical outcomes. In addition, deep learning techniques have been proved helpful for auto-segmentation of targets and capture of features, which provide the possibility of maximum use of image data for clinical management.
Integrating quantitative measurements from radiology and pathology data for personalized medicine remains challenging in terms of robust data provenance, optimization, tuning parameters for machine learning and deep learning algorithms, and standard process procedures. Radiomics can successfully extract information from radiology images and establish image biomarkers for predictive and prognostic modeling. In contrast to radiomics, pathomics analyzes digital microscopy images of tissue, cells, and subcellular structures. Deep learning algorithms can help automatically extract proposed features or develop new features that are hard to explain.
Currently, a computational method for extracted features should consider the discrimination ability and need to consider explainability. Therefore, a deep learning model, treated as a “black box,” should be used for the isolated process instead of all procedures.
This topic aims to focus on applying data mining in radiology and pathology images using deep learning or machine learning techniques. We welcome the submission of original research and review articles that include image analysis in clinical studies and applications and technologies or discoveries in computational approaches.
• Computational approaches to analyzing images for noninvasive diagnosis of disease.
• Computational approaches to analyzing images for predicting survival outcomes.
• Computational approaches to analyzing images for predicting treatment response.
• Deep learning-based analysis of radiological-pathological images for biomarkers.
• Comparisons of different computational approaches for the same purpose in medicine.
• Correlations between radiomics, pathomics, and genomics.
• Integration of radiomics and pathomics for the establishment of prediction models.