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      Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

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          Abstract

          Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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          Most cited references87

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Gradient-based learning applied to document recognition

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              A survey on deep learning in medical image analysis

              Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
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                Author and article information

                Contributors
                Journal
                Front Oral Health
                Front Oral Health
                Front. Oral. Health
                Frontiers in Oral Health
                Frontiers Media S.A.
                2673-4842
                11 January 2022
                2021
                : 2
                : 794248
                Affiliations
                [1] 1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki, Finland
                [2] 2Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa , Vaasa, Finland
                [3] 3Department of Pathology, University of Helsinki , Helsinki, Finland
                [4] 4Institute of Biomedicine, Pathology, University of Turku , Turku, Finland
                [5] 5Department of Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital , Helsinki, Finland
                [6] 6Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital , Stockholm, Sweden
                Author notes

                Edited by: Cesare Piazza, University of Brescia, Italy

                Reviewed by: Filippo Marchi, San Martino Hospital (IRCCS), Italy; Alberto Paderno, University of Brescia, Italy

                *Correspondence: Rasheed Omobolaji Alabi rasheed.alabi@ 123456helsinki.fi

                This article was submitted to Oral Cancers, a section of the journal Frontiers in Oral Health

                Article
                10.3389/froh.2021.794248
                8786902
                35088057
                5dd47a72-3c18-441e-a166-489e11aaafaf
                Copyright © 2022 Alabi, Almangush, Elmusrati and Mäkitie.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 October 2021
                : 13 December 2021
                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 88, Pages: 11, Words: 8273
                Categories
                Oral Health
                Original Research

                machine learning,deep learning,oral cancer,prognostication,precision medicine,precise surgery

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