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Yali Nie
Researcher at Mid Sweden University
Publications - 6
Citations - 58
Yali Nie is an academic researcher from Mid Sweden University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 2, co-authored 3 publications receiving 12 citations.
Papers
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Proceedings ArticleDOI
Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks
TL;DR: The test results indicate that the mean average precision (mAP) of Yolo can exceed 0.82 with a training set of only 200 images, proving that this method has great advantages for detecting melanoma in lightweight system applications.
Proceedings ArticleDOI
Deep Melanoma classification with K-Fold Cross-Validation for Process optimization
TL;DR: Results reveal that the model built by the approach proposed in this paper can effectively achieve a better prediction and enhance the accuracy of the model, which proves that K-Fold can achieve better performance on a small skin cancer dataset.
Journal ArticleDOI
A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss
TL;DR: Based on the success of the transformer network in natural language processing and the deep convolutional neural network (DCNN) in computer vision, the authors proposed an end-to-end CNN transformer hybrid model with a focal loss (FL) function to classify skin lesion images.
Journal ArticleDOI
Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning
TL;DR: This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this, and discusses advancements in deep learning-based solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems.
Proceedings ArticleDOI
Ensembling CNNs for dermoscopic analysis of suspicious skin lesions
Yali Nie,Matteo Ferro,Paolo Sommella,Marco Carratu,Sara Cacciapuoti,Giuseppe Di Leo,Jan Lundgren,Gabriella Fabbrocini +7 more
TL;DR: In this paper, the authors used an original ensembling of multiple CNNs as feature extractors able to detect and measure skin lesions atypical criteria according to the well-known diagnostic method 7-Point Check List.