Deep features to classify skin lesions

By Jeremy Kawahara, Aïcha BenTaieb, Ghassan Hamarneh. IEEE International Symposium on Biomedical Imaging, 2016

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/dbb602ef9409e30767446784823971ef520eb1a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. P. Heng
2. Qi-Yan Dou
3. Jing Qin
4. Hao Chen
5. Lequan Yu
6. Francesco Ciompi
7. J. A. van der Laak
8. Geert J. S. Litjens

In Epidemiology

1. LinLin Shen
2. Yuexiang Li
3. Qi-Yan Dou

In Oncology

1. P. Heng
2. Hao Chen
3. LinLin Shen
4. Yuexiang Li
5. Lequan Yu
6. Balázs Harangi

Citations in the corpus

• Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (2019, background): “To get some more benefits over other CNN-based systems, authors perfectly decided to fully utilize both local and global contextual features [43], coming from deeper and higher layers of network respectively.”
• A survey on deep learning in medical image analysis (2017, background): “(2016) 10-class classification photographic images CNN Extending Kawahara et al. (2016a) now training”
• Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (2017, methodology): “based on AlexNet [24] to extract representative features of melanoma [25].”
• Skin lesion classification from dermoscopic images using deep learning techniques (2017, background): “[14] explores the idea of using a pretrained ConvNet as a feature extractor rather than training a CNN from scratch.”
• Skin Cancer Detection: A Review Using Deep Learning Techniques (2021, methodology): “[54] AK/melanocytic nevus/BCC/SK/SCC CNN 1300 skin lesion images Mean subtraction for each image, pooled multi-scale feature extraction process and pooling in augmented-feature space Accuracy (81.”
• Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review (2018, methodology): “Kawahara et al [25] used a linear classifier to classify 10 different skin lesions.”
• Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (2019, background): “[22] demonstrated how using features from a neural network pretrained over ImageNet to classify skin diseases outperformed approaches that rely on handcrafted human engineered features [21], [23].”
• Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network (2017, methodology): “employed a fully convolutional network to extract multi-scale features for melanoma recognition [31].”