Skin Lesion Classification Using Additional Patient Information.
In this paper, we describe our method for skin lesion classification. The goal is to classify skin lesions based on dermoscopic images to several diagnoses' classes presented in the HAM (Human Against Machine) dataset: melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic kerat...
| Publicado en: | BioMed Research International pp. 1 - 7 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/12/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149778840&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149778840 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/12/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149778840 149778840 149778840 10.1155/2021/6673852 149778840 ppf: 1 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Skin Lesion Classification Using Additional Patient Information. aug: au: Sun, Qilin Huang, Chao Chen, Minjie Xu, Hui Yang, Yali affil: Department of Dermatology, Shanghai Ninth Hospital affiliated to Shanghai Jiao Tong University, School of Medicine, No. 639, Manufacturing Bureau Road, Huangpu District, Shanghai 200011, China sug: subj: Skin Diseases Classification Health Information Patient Education Human Metadata Algorithms Sensitivity and Specificity Microscopy Methods Minimum Data Set Carcinoma, Basal Cell Classification Nevi and Melanomas Classification Keratosis, Actinic Classification Skin Neoplasms Classification ab: In this paper, we describe our method for skin lesion classification. The goal is to classify skin lesions based on dermoscopic images to several diagnoses' classes presented in the HAM (Human Against Machine) dataset: melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), and vascular lesion (VASC). We propose a simplified solution which has a better accuracy than previous methods, but only predicted on a single model that is practical for a real-world scenario. Our results show that using a network with additional metadata as input achieves a better classification performance. This metadata includes both the patient information and the extra information during the data augmentation process. On the international skin imaging collaboration (ISIC) 2018 skin lesion classification challenge test set, our algorithm yields a balanced multiclass accuracy of 88.7% on a single model and 89.5% for the embedding solution, which makes it the currently first ranked algorithm on the live leaderboard. To improve the inference accuracy. Test time augmentation (TTA) is applied. We also demonstrate how Grad-CAM is applied in TTA. Therefore, TTA and Grad-CAM can be integrated in heat map generation, which can be very helpful to assist the clinician for diagnosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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