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      A review on brain tumor segmentation based on deep learning methods with federated learning techniques

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          The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

          The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
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            ImageNet classification with deep convolutional neural networks

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              Fully convolutional networks for semantic segmentation

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Computerized Medical Imaging and Graphics
                Computerized Medical Imaging and Graphics
                Elsevier BV
                08956111
                December 2023
                December 2023
                : 110
                : 102313
                Article
                10.1016/j.compmedimag.2023.102313
                38011781
                b5a27f17-b2df-4650-b9d4-c79548d5ac12
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by/4.0/

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