Book ChapterDOI
Skin Lesion Classification Using Deep Learning
Aditya Bhardwaj,Priti P. Rege +1 more
- pp 575-589
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TLDR
In this paper, a neural network for skin cancer classification is proposed based on merging of top-N performing models used as a feature extractor and a SVM to facilitate classification of diseases.Abstract:
Skin cancer is a common disease and considered to be one of the most prevalent forms of cancer found in humans. Over the years various imaging techniques have shown improvement and reliability in diagnosis process of Skin Cancer. However, quite a few challenges are being faced in generating reliable and well-timed results as adoption of clinical computer aided systems is still limited. With the recent emergence of learning algorithms and its application in computer vision suggests a need for combination of sufficient clinical expertise and systems to achieve better results. Here we attempt to bridge the gap by mining collective knowledge contained in current Deep Learning Techniques to discover underlying principles for designing a neural network for skin disease classification. The solution is based upon merging of top-N performing models used as a feature extractor and a SVM to facilitate classification of diseases. Final model gave 86% accuracy on ISIC 2019 dataset along with high precision and recall values of 0.8 and 0.6, respectively.read more
Citations
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Journal ArticleDOI
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
Moloud Abdar,Maryam Samami,Sajjad Dehghani Mahmoodabad,Thang Doan,Bogdan Mazoure,Reza Hashemifesharaki,Li Liu,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +12 more
TL;DR: Wang et al. as mentioned in this paper applied three uncertainty quantification methods to deal with uncertainty during skin cancer image classification, i.e., Monte Carlo (MC), Ensemble MC (EMC) and Deep Ensemble (DE).
Journal ArticleDOI
Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures
TL;DR: The authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification and recommend several new approaches.
Journal ArticleDOI
Adversarial multimodal fusion with attention mechanism for skin lesion classification using clinical and dermoscopic images
TL;DR: Zhang et al. as mentioned in this paper proposed an adversarial multimodal fusion with attention mechanism (AMFAM) to perform multi-modal skin lesion classification, which adopts a discriminator that uses adversarial learning to enforce the feature extractor to learn the correlated information explicitly.
Journal ArticleDOI
Hierarchy-aware contrastive learning with late fusion for skin lesion classification
TL;DR: Wang et al. as mentioned in this paper proposed Hierarchy-Aware Contrastive Learning with Late Fusion (HAC-LF) to improve the overall performance of multi-class skin classification.
Journal ArticleDOI
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning
TL;DR: This paper proposes FairDisCo, a disentanglement deep learning framework with contrastive learning that utilizes an additional network branch to remove sensitive attributes from representations for fairness and another contrastive branch to enhance feature extraction.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
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