Digital and Computational Pathology

This conference will address digital and computational pathology, from acquisition of pathology data to its management, analysis, and interpretation by observers. With the recent advances in whole slide scanners and novel instrumentation for multispectral, multiparametric tissue imaging the use of digital pathology data is growing in importance. Both the pre-clinical and clinical modeling of disease states are addressed by the developing field of computational pathology. The evolving concepts of human intelligence-artificial intelligence interactions in our understanding of image data are foundational in computational pathology. There is evidence that digital and computational pathology can improve diagnosis and grading of cancer and other pathology tasks, but there are still limitations and challenges that must be addressed before it can be fully incorporated into the clinical workflow.

Although there has been great progress in the development and application of computational pathology methods over recent years, there are several significant computational challenges specific to pathology imaging that distinguish it from its radiological counterpart. There are also unique challenges in terms of how digitized pathology specimens and correlated data are presented to, modified and interpreted by clinicians and computers.

We invite submissions that address specific problems related to image acquisition, display, interpretation, computer-aided diagnosis, and quantitative image analysis of pathology specimens. We particularly welcome contributions that identify and address challenges encountered in digital pathology imaging as well as in new approaches for image capture and analysis. Suggested topics include:

Image acquisition, storage, and display

• acquisition, storage, display and processing of digital microscopy images
• image mosaicking of nontraditional near-real-time microscopy (OCT, confocal)
• multispectral imaging
• high-dimensional multiplexed staining and imaging of tissues
• multi-focus volume imaging
• compression
• methodologies for the objective technical assessment of digital pathology systems including color calibration
• whole slide imaging
• strategies for data storage and remote processing.

Quantitative image analysis

• computer-aided diagnosis, prognosis and predictive analysis
• automated quantification of tissue biomarkers
• grading and classification of pathology images
• segmentation of cellular and tissue structures
• shape analysis and morphology in pathology imaging
• architectural feature extraction and quantification
• multispectral- and volume-based segmentation
• content-based image retrieval
• high-performance computing for whole-slide tissue image analysis
• multi-stain and multiplexed image analysis
• correlative microscopy
• understanding of image data across scale
• machine learning trends in digital pathology: handcrafted features versus deep learning.

Information fusion

• radiology-pathology registration and fusion
• registration of multiple stained tissue microscopy images
• integration of digital image features with 'omics' data for fused diagnostics.

Digital/computational pathology and the pathologist

• observer performance, human factors, reading strategies, and diagnostic interpretation issues
• remote consultation
• metrics, variability and standardization issues unique to digital pathology
• methodologies for the objective technical assessment of digital pathology systems
• optical probe tracking and visualization tools
• PACS and new DICOM standards for histopathology
• making the case for clinical digital pathology systems in pathology practice.