Análise de lesões de pele usando redes generativas adversariais

Orientador: Sandra Eliza Fontes de AvilaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Melanoma é a forma mais letal de câncer de pele. Devido a possibilidade de metástase, o diagnóstico precoce é crucial para aumentar a taxa de sobrevivência dos pacientes. A análise automatizada de lesões de pele pode ter um papel importante ao alcançar pessoas sem acesso a especialistas. Porém, desde que técnicas de aprendizado profundo se tornaram o estado-da-arte para análise de lesões de pele, os dados se tornaram um fator decisivo para avançar as soluções. O objetivo principal dessa tese de mestrado é tratar dos problemas que surgem por lidarmos com poucos dados nesse contexto médico. Na primeira parte, usamos redes generativas adversariais para gerar dados sintéticos para aumentar o conjunto de treino dos nossos modelos de classificação para elevar a performance. Nosso método é capaz de gerar imagens de lesão de pele em alta-resolução com significado clínico, que quando usadas para compor o conjunto de treino de redes de classificação, consistentemente melhoram a performance em diferentes cenários, para diferentes dados. Também investigamos como nossos modelos de classificação interpretam as amostras sintéticas, e como elas são capazes de ajudar na generalização do modelo. Finalmente, analisamos um problema que surge por termos poucos, relativamente pequenos conjuntos de dados que são reusados repetidamente na literatura: bias. Para isso, planejamos experimentos para estudar como nossos modelos usam os dados, verificando como ele explora correlações corretas (com base em algoritmos médicos), e espúrias (com base em artefatos introduzidos durante a aquisição das imagens). Surpreendentemente, mesmo sem contar com nenhuma informação clínica sobre a lesão sendo diagnosticada, nossos modelos de classificação apresentaram performance muito melhor que o acaso (competindo até mesmo com benchmarks de especialistas), sugerindo performances altamente infladasAbstract: Melanoma is the most lethal type of skin cancer. Due to the possibility of metastasis, early diagnosis is crucial to increase the survival rate of those patients. Automated skin lesion analysis can play an important role by reaching people that do not have access to a specialist. However, since deep learning became the state-of-the-art for skin lesion analysis, data became a decisive factor to push the solutions further. The core objective of this Master thesis is to tackle the problems that arise by having limited datasets. In the first part, we use generative adversarial networks (GANs) to generate synthetic data to augment our classification model's training datasets to boost performance. Our method is able to generate high-resolution clinically-meaningful skin lesion images, that when compound our classification model's training dataset, consistently improved the performance in different scenarios, for distinct datasets. We also investigate how our classification models perceived the synthetic samples, and how they are able to aid the model's generalization. Finally, we investigate a problem that usually arises by having few, relatively small datasets that are thoroughly re-used in the literature: bias. For this, we designed experiments to study how our models' use of data, verifying how it exploits correct (based on medical algorithms), and spurious (based on artifacts introduced during image acquisition) correlations. Disturbingly, even in absence of any clinical information regarding the lesion being diagnosed, our classification models presented much better performance than chance (even competing with specialists benchmarks), highly suggesting inflated performancesMestradoCiência da ComputaçãoMestre em Ciência da Computação134271/2017-3CNP

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

Computational Diagnosis of Skin Lesions from Dermoscopic Images using Combined Features

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm

SkinCAN AI: A deep learning-based skin cancer classification and segmentation pipeline designed along with a generative model

The rarity of Melanoma skin cancer accounts for the dataset collected to be limited and highly skewed, as benign moles can easily mimic the impression of the melanoma-affected area. Such an imbalanced dataset makes training any deep learning classifier network harder by affecting the training stability. We have an intuition that synthesizing such skin lesion medical images could help solve the issue of overfitting in training networks and assist in enforcing the anonymization of actual patients. Despite multiple previous attempts, none of the models were practical for the fast-paced clinical environment. In this thesis, we propose a novel pipeline named SkinCAN AI, inspired by StyleGAN but designed explicitly considering the limitations of the skin lesion dataset and emphasizing the requirement of a faster optimized diagnostic tool that can be easily inferred and integrated into the clinical environment. Our SkinCAN AI model is equipped with its module of adaptive discriminator augmentation that enables limited target data distribution to be learned and artificial data points to be sampled, which further assist the classifier network in learning semantic features. We elucidate the novelty of our SkinCAN AI pipeline by integrating the soft attention module in the classifier network. This module yields an attention mask analyzed by DenseNet201 to focus on learning relevant semantic features from skin lesion images without using any heavy computational burden of artifact removal software. The SkinGAN model achieves an FID score of 0.622 while allowing its synthetic samples to train the DenseNet201 model with an accuracy of 0.9494, AUC of 0.938, specificity of 0.969, and sensitivity of 0.695. We provide evidence in our thesis that our proposed pipelines outperform other state-of-the-art existing networks developed for this task of early diagnosis