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Deep Learning Approaches to Cancer Diagnosis using Histopathological Images
Published in Hassan Ugail, Deep Learning in Visual Computing, 2022
In parallel, Computer-Aided Diagnosis (CAD) methods based on deep learning techniques have recently had an impressive impact on the digital pathology field, which improved natural image recognition performances. The ongoing development in digital pathology allows for automated image analysis methods to support pathologists at such tasks and to increase the reliability of quantitative assessments.
Cancer registry and big data exchange
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Zhenwei Shi, Leonard Wee, Andre Dekker
Last, a rapidly developing data source is the result of digital pathology and high-throughput specimen analysis from medical laboratories. This includes genomics, proteomics, metabolomics, histologics, and hematologics.
An efficient stacked ensemble model for the detection of COVID-19 and skin cancer using fused feature of transfer learning and handcrafted methods
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Over the years, many computer-aided systems have been developed to diagnose and detect human disease by using medical images. The most recent imaging modalities rely on high-resolution imaging to provide radiologists with multi-oriented representations, which helps them with a clinical diagnosis and perform accurate predictions and treatment for a patient. Ultrasound, magnetic resonance imaging (MRI), x-ray computed tomography and endoscopy are some of the most common modalities for medical imaging (Chowdhary and Acharjya 2020; Houssein et al. 2021c). Due to the fast development of medical imaging technologies, the field of digital pathology, which concentrates on the analysis and management of image information produced by these technologies, is expanding quickly (Mormont et al. 2018). Digital pathology with machine learning and deep learning technologies holds considerable potential for medical practices (diagnostic medicine and disease prediction) and research studies related to biomedical (Foucart et al. 2019; Fan et al. 2021; Houssein et al. 2022b).
Intelligent framework for brain tumor grading using advanced feature analysis
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Geethu Mohan, Monica Subashini M
The gold standard for grading and typing gliomas is histopathology (Wesseling et al. 2011). But histological classification of gliomas is often associated with notable inter-observer variability. The machine learning and image analysis algorithms are intruding into digital pathology field forming a new domain of computational pathology. This potentially improves a pathologist’s workflow and also supports remote education and consultation. The qualitative visual analysis of a WSI is limited by diagnostic errors, lack of standardisation and remarkable cognitive load over the pathologist for manually evaluating millions of cells across multiple slides in a day.