Deep learning applied to the classification of skin lesions

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSkin cancer has been a global health issue and its diagnosis is a challenge in the medical field. Among all the types of skin cancer, melanoma is the worst and can be lethal if not early treated. The use of deep learning techniques, specifically, convolutional neural networks can help to improve the accuracy and speed up the classification of skin lesions. In this work, we aim to employ different image preprocessing techniques, various convolutional neural network models, data augmentation, and ensemble techniques to compare their results and provide an analysis of the data obtained. To achieve that, it was performed several experiments combining different image preprocessing techniques, which, paired with data augmentation strategies, aim to enhance the accuracy and reliability of the classification models. Additionally, three ensemble methods were tested to improve the classification systems’ robustness and reliability by gathering the strengths of each model. Our best result was the ensemble of EfficientNet-B2, EfficientNet-B5, and ResNeSt101 models with the application of data augmentation, and the combination of color constancy and hair removal techniques. This combined approach achieved a balanced accuracy of0.8132. By offering insights into the challenges faced, methodologies employed, and results obtained, this story aims to serve as a guide for researchers and practitioners aiming to advance the field of skin lesion classification using deep learning. Keywords: Deep Learning; Skin Lesion Classification; Image preprocessing.O câncer de pele é um problema de saúde global e seu diagnóstico é um desafio na área médica. Entre todos os tipos de câncer de pele, o melanoma é o pior e pode ser letal se não tratado precocemente. O uso de técnicas de deep learning, especificamente, redes neurais convolucionais, pode ajudar a melhorar a precisão e acelerar a classificação de lesões de pele. Neste trabalho, buscamos empregar diferentes técnicas de pré-processamento de imagens, vários modelos de redes neurais convolucionais, data augmentation e técnicas de ensemble para comparar seus resultados e fornecer uma análise dos dados obtidos. Para isso, foram realizados vários experimentos combinando diferentes técnicas de préprocessamento de imagens, que, combinadas com estratégias de data augmentation, visam melhorar a precisão e confiabilidade dos modelos de classificação. Além disso, três métodos de ensemble foram testados para melhorar a robustez e confiabilidade dos sistemas de classificação, reunindo os pontos fortes de cada modelo. Nosso melhor resultado foi o ensemble dos modelos EfficientNet-B2, EfficientNet-B5 e ResNeSt101 com a aplicação de data augmentation e a combinação de técnicas de color constancy e remoção de pelos. Esta abordagem alcançou uma balanced accuracy de 0,8132. Ao oferecer insights sobre as metodologias empregadas e resultados obtidos, este estudo visa servir como um guia para pesquisadores e profissionais que buscam avançar no campo da classificação de lesões cutâneas usando aprendizado profundo

AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

Artificial Intelligence (AI) is widely used in dermatology to analyze trichoscopy imaging and assess Alopecia Areata (AA) and scalp hair problems. From this viewpoint, the Attention-based Balanced Multi-Tasking Ensembling Deep (AB-MTEDeep) network was developed, which combined the Faster Residual Convolutional Neural Network (FRCNN) and Long Short-Term Memory (LSTM) network with cross residual learning to classify scalp images into different AA classes. This article presents a new data augmentation model called AA-Generative Adversarial Network (AA-GAN) to produce a huge number of images from a set of input images. The structure of AA-GAN and its loss functions are comparable to those of standard GAN, which encompasses a generator and a discriminator network. To generate high-quality AA structure-based images, the generator was trained to extract the 2D orientation and confidence maps along with the bust depth map from real hair and scalp images. The discriminator was also used to separate real from generated images, which were provided as feedback to the generator to create synthetic images that are extremely close to the real input images. The created images were used to train the AB-MTEDeep model for AA classification. Finally, the experimental results exhibited that the AA-GAN-AB-MTEDeep achieved 96.94% accuracy

Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering

Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery framework for automatically discovering new semantic classes from dermoscopy image datasets based on the knowledge of known classes. Specifically, we first use contrastive learning to learn a robust and unbiased feature representation based on all data from known and unknown categories. We then propose an uncertainty-aware multi-view cross pseudo-supervision strategy, which is trained jointly on all categories of data using pseudo labels generated by a self-labeling strategy. Finally, we further refine the pseudo label by aggregating neighborhood information through local sample similarity to improve the clustering performance of the model for unknown categories. We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories. We also further validated the effectiveness of the different modules through extensive ablation experiments. Our code will be released soon.Comment: 10 pages, 1 figure,Accepted by miccai 202

Application of deep learning general-purpose neural architectures based on vision transformers for ISIC melanoma classification

The field of computer vision has for years been dominated by Convolutional Neural Networks (CNNs) in the medical field. However, there are various other Deep Learning (DL) techniques that have become very popular in this space. Vision Transformers (ViTs) are an example of a deep learning technique that has been gaining in popularity in recent years. In this work, we study the performance of ViTs and CNNs on skin lesions classification tasks, specifically melanoma diagnosis. We compare the performance of ViTs to that of CNNs and show that regardless of the performance of both architectures, an ensemble of the two can improve generalization. We also present an adaptation to the Gram-OOD* method (detecting Out-of-distribution (OOD) using Gram matrices) for skin lesion images. A rescaling method was also used to address the imbalanced dataset problem, which is generally inherent in medical images. The phenomenon of super-convergence was critical to our success in building models with computing and training time constraints. Finally, we train and evaluate an ensemble of ViTs and CNNs, demonstrating that generalization is enhanced by placing first in the 2019 and third in the 2022 ISIC Challenge Live. Leaderboard (available at \href{https://challenge.isic-archive.com/leaderboards/live/}{https://challenge.isic-archive.com/leaderboards/live/})