SUB TRACK:  Computational Pathology, computational analysis, diagnose disease, automatically, Whole slide image, machine learning, deep learning, artificial intelligence, image analysis, histopathological glass slide, microscope, slide scanners, scanners, techniques, digital image analysis, diagnostics. precise diagnoses, patient-specific treatments, disease pathogenesis, disease stratification, data technologies,  tissue features, individual cells, inference, prediction algorithms,  laboratory personnel, Computational pathology

A field of pathology that uses computer analysis to examine patient samples using a wide range of techniques in order to understand disease. This work focuses on information extraction from digital pathology images and their accompanying meta-data, often using AI techniques like deep learning.

The fields of pathology and laboratory medicine are being revolutionised by improvements in high-throughput laboratory and health information technologies. A crucial and significant development in the provision of healthcare will undoubtedly be the capacity to extract clinically useful knowledge using computational methods from complex, high-dimensional laboratory and clinical (digital) data, leading to more accurate diagnoses, disease stratification, and patient-specific treatment selection.

Using a variety of raw data sources, such as clinical electronic medical records, laboratory data, including “-omics,” and imaging, computational pathology is an approach to diagnosis. Pathology can enhance the dissemination of medically useful knowledge over time and across populations and boost the effectiveness of health care delivery by creating the tools to harness those drives.

Bright field and fluorescence scanners are used by the Computational Pathology Laboratory (DCPL) to acquire high resolution images of tissue slides, allowing researchers to see specifics of the morphologic and spectral properties of cells and/or tumour regions.

Use of advanced computational techniques:
A crucial component of the promise of CPATH has been the employment of cutting-edge computational techniques like deep learning (DL) and machine learning (ML). Artificial intelligence is demonstrated by both ML and its subset DL (AI). An associated idea is ML-powered image analysis, which enables incredibly precise image classification or segmentation. Once these picture attributes are connected with other sorts of patient information than the image itself, the outputs of these computer-based tools could then be included in a comprehensive CPATH process. Despite the fact that this kind of research shows enormous promise for a paradigm shift in healthcare, numerous obstacles still stand in the way of its widespread clinical application.

A pathologist is a doctor who analyses body parts and bodily tissues. Additionally, he or she is in charge of running lab testing. A pathologist is a crucial part of the treatment team who assists other medical professionals in making diagnosis.

Computational pathology offers a range of benefits that enhance the practice of pathology and improve patient care. Some of these benefits include: 

1. Enhanced Accuracy: Computational pathology tools can assist pathologists in identifying and analyzing patterns in large datasets, leading to more accurate diagnoses and prognoses.
2. Efficiency: Automation of repetitive tasks such as slide scanning, image analysis, and data extraction can save time for pathologists, allowing them to focus more on complex cases and patient care.
3. Standardization: Computational pathology helps standardize diagnostic criteria and methodologies, reducing variability among pathologists and improving consistency in diagnoses.
4. Access to Expertise: Telepathology, a subset of computational pathology, enables remote consultation and access to expert opinions regardless of geographic location, improving patient care in underserved areas.
5. Research Advancement: Computational pathology facilitates the analysis of large-scale datasets, enabling researchers to discover new biomarkers, understand disease mechanisms, and develop more effective treatments.
6. Personalized Medicine: By integrating computational pathology with genomic data and other clinical information, healthcare providers can tailor treatment plans to individual patients, leading to more personalized and effective therapies.
7. Cost Reduction: Streamlining pathology workflows through automation and digitalization can reduce operational costs associated with slide storage, transport, and retrieval, while also optimizing resource utilization.
8. Education and Training: Digital pathology platforms allow for the creation and sharing of educational materials, virtual slides, and interactive tutorials, enhancing the training of medical students, residents, and practicing pathologists.
9. Scalability: Computational pathology solutions can scale to handle increasing volumes of data and cases, supporting the growing demands of healthcare systems worldwide.
10. Quality Improvement: By providing feedback mechanisms and performance metrics, computational pathology tools can help pathologists continuously improve their skills and maintain high standards of quality in diagnostic practice.