SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification

Skin cancer is a treatable disease if discovered early. We provide a production-specific solution to the skin cancer classification problem that matches human performance in melanoma identification by training a vision transformer on melanoma medical images annotated by experts. Since inference cost, both time and memory wise is important in practice, we employ knowledge distillation to obtain a model that retains 98.33% of the teacher's balanced multi-class accuracy, at a fraction of the cost. Memory-wise, our model is 49.60% smaller than the teacher. Time-wise, our solution is 69.25% faster on GPU and 97.96% faster on CPU. By adding classification heads at each level of the transformer and employing a cascading distillation process, we improve the balanced multi-class accuracy of the base model by 2.1%, while creating a range of models of various sizes but comparable performance. We provide the code at https://github.com/Longman-Stan/SkinDistilVit.Comment: Accepted at ICANN 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/})

Measles Rash Identification Using Residual Deep Convolutional Neural Network

Measles is extremely contagious and is one of the leading causes of vaccine-preventable illness and death in developing countries, claiming more than 100,000 lives each year. Measles was declared eliminated in the US in 2000 due to decades of successful vaccination for the measles. As a result, an increasing number of US healthcare professionals and the public have never seen the disease. Unfortunately, the Measles resurged in the US in 2019 with 1,282 confirmed cases. To assist in diagnosing measles, we collected more than 1300 images of a variety of skin conditions, with which we employed residual deep convolutional neural network to distinguish measles rash from other skin conditions, in an aim to create a phone application in the future. On our image dataset, our model reaches a classification accuracy of 95.2%, sensitivity of 81.7%, and specificity of 97.1%, indicating the model is effective in facilitating an accurate detection of measles to help contain measles outbreaks

Classification of Melanocytic Nevus Images using BigTransfer (BiT)

Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods.Comment: 5 pages, 3 figure

Deep Convolutional Neural Network Ensembles using ECOC

Deep neural networks have enhanced the performance of decision making systems in many applications including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is very high or the performance gain obtained is not very significant. In this paper, we analyse error correcting output coding (ECOC) framework to be used as an ensemble technique for deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a combinatory technique which is shown to achieve the highest classification performance amongst all.Comment: 13 pages double column IEEE transactions styl

A deep analysis on high resolution dermoscopic image classification

[EN] Convolutional neural networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). As in many other medical imaging domains, state-of-the-art methods take advantage of architectures developed for other tasks, frequently assuming full transferability between enormous sets of natural images (e.g. ImageNet) and dermoscopic images, which is not always the case. A comprehensive analysis on the effectiveness of state-of-the-art deep learning techniques when applied to dermoscopic image analysis is provided. To achieve this goal, the authors consider several CNNs architectures and analyse how their performance is affected by the size of the network, image resolution, data augmentation process, amount of available data, and model calibration. Moreover, taking advantage of the analysis performed, a novel ensemble method to further increase the classification accuracy is designed. The proposed solution achieved the third best result in the 2019 official ISIC challenge, with an accuracy of 0.593.Juan Maroñas is supported by grant FPI-UPV, grant agreement No 825,111 DeepHealth Project, and by the Spanish National Ministry of Education through grant RTI2018-098091-B-I00. The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valencia (2014-2020) under project Sistemas de frabricación inteligentes para la indústria 4.0 (grant agreement IDIFEDER/2018/025).Pollastri, F.; Parreño Lara, M.; Maroñas-Molano, J.; Bolelli, F.; Paredes Palacios, R.; Ramos, D.; Grana, C. (2021). A deep analysis on high resolution dermoscopic image classification. IET Computer Vision. 15(7):514-526. https://doi.org/10.1049/cvi2.1204851452615

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