نتایج جستجو برای: classification lesion
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PURPOSE The Jefferson classification has been used to localize cavernous sinus lesions. However, this classification occasionally showed dissociation between identified localization and clinical findings. We investigated the clinical applicability of the newly proposed Ishikawa classification based on serial topographic sections of human cavernous sinus and the clinical findings. METHODS In t...
Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system an...
OBJECTIVE To present the clinical relevance of the anatomical classification of the neovascular form of Age-Related Macular Degeneration (AMD). METHODS Critical analysis of the current situation in the management of patients with neovascular AMD, by reviewing the available scientific evidence with regards to the classification of the types of neovascular lesion by angiography and optical cohe...
A connectionist architecture is presented that is able to perform focal lesions classification in MR images of brain tissues affected by multiple sclerosis disease. Images are first segmented using a fuzzy technique; then the images of each cluster are processed in order to classify and label non-pathologic tissues and to locate all possible candidates to be sclerosis lesions. Finally the neura...
© 2011 The Authors. doi: 10.2340/00015555-1175 Journal Compilation © 2011 Acta Dermato-Venereologica. ISSN 0001-5555 Oral lesions are rarely reported in paucibacillary forms of leprosy. We report here a case with an erythematous hyposensitive lesion in the palate and no skin lesions. In addition to routine tests, biopsies of the lesion in the palate and of clinically normal surrounding areas we...
Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting
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