Computer-Assisted Digital Pathology in Diagnostics
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4550
Special Issue Editor
Special Issue Information
Dear Colleagues,
Diagnostic pathology is a medical specialty focusing on the examination of disease via microscopic examination of abnormal body tissues (usually with histopathology) and diagnosis based on the morphological characteristics of said tissues. Within diagnostic pathology, digital pathology has played an increasingly crucial role. It involves the digitization, transfer, and storage of histologically stained tissue section slides at high resolution. With integration of digital slides into the pathology workflow, advanced algorithms and computer-aided diagnostic techniques have extended the frontiers of the pathologist's view beyond a microscopic slide and have enabled true utilization and integration of knowledge beyond human limits and boundaries. In particular, these technologies serve as an enabling platform for the application of computer-assisted technologies such as artificial intelligence (AI), deep learning, and machine learning in digital pathology. AI already enables pathologists to identify unique imaging markers associated with disease processes with the goal of improving early detection, determining prognosis, and selecting treatments most likely to be effective. This allows pathologists to serve more patients while maintaining diagnostic and prognostic accuracy.
Computer-assisted diagnostic pathology is slated to immensely impact clinical practice, but especially for oncology and precision medicine. Much like the evolution of the efficiency and effectiveness of radiology, the pressure on pathologists to reduce the turnaround time and develop more efficient workflows is trending towards digitalization. This digital innovation has the potential to change the way diagnosis is carried out—in particular, the added benefits of shared images and data, increased efficiency and integrated diagnostics, modernization of pathology workflows to improve patient care and safety, increased collaboration through multidisciplinary and disease-specific patient-care conferences, improved accountability (on behalf of the physician, who makes the final clinical decision), and cost savings by optimizing staff performance. Overall, computer-aided diagnostic pathology can automate and standardize many of the tasks that are manual and subjective. This Special Issue covers (but is not limited to) topics on machine learning and deep learning methods with their applications in:
- grading and classification of pathology images;
- multi-stain and multiplexed image analysis;
- image analysis of anatomical structures/functions and lesions;
- architectural feature extraction and quantification;
- stain normalization/standardization;
- radiology-pathology registration and fusion;
- segmentation of cellular and tissue structures;
- multi-modality fusion for analysis diagnosis and intervention;
- content-based image retrieval;
- computer-aided diagnosis prognosis and predictive analysis;
- metrics variability and standardization issues unique to digital pathology;
- whole-slide image analysis;
- detection or identification of predictive and prognostic tissue biomarkers;
- observer performance human factors reading strategies and diagnostic interpretation issues;
- immunohistochemistry scoring;
- automated quantification of tissue biomarkers;
- registration of multiple stained tissue microscopy images;
- comparison of quantitative results with qualitative results.
Dr. Muhammad Khalid Khan Niazi
Guest Editor
Manuscript Submission Information
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Keywords
- diagnostic pathology
- digital pathology
- digital slides
- histologically stained tissue
- computer-aided diagnostic techniques
- artificial intelligence (AI)
- deep learning
- machine learning
- unique imaging markers
- prognostic
- oncology
- precision medicine
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