Triagem robusta de melanoma : em defesa dos descritores aprimorados de nível médio

Orientadores: Eduardo Alves do Valle Junior, Sandra Eliza Fontes de AvilaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Melanoma é o tipo de câncer de pele que mais leva à morte, mesmo sendo o mais curável, se detectado precocemente. Considerando que a presença de um dermatologista em tempo integral não é economicamente viável para muitas cidades e especialmente em comunidades carentes, ferramentas de auxílio ao diagnóstico para a triagem do melanoma têm sido um tópico de pesquisa ativo. Muitos trabalhos existentes são baseados no modelo Bag-of-Visual-Words (BoVW), combinando descritores de cor e textura. No entanto, o modelo BoVW vem se aprimorando e hoje existem várias extensões que levam a melhores taxas de acerto em tarefas gerais de classificação de imagens. Estes modelos avançados ainda não foram explorados para rastreio de melanoma, motivando assim este trabalho. Aqui nós apresentamos uma nova abordagem para rastreio de melanoma baseado nos descritores BossaNova, que são estado-da-arte, mostrando resultados muito promissores, com uma AUC de 93,7%. Este trabalho também propõe uma nova estratégia de pooling espacial especialmente desenhada para rastreio de melanoma. Outra contribuição dessa pesquisa é o uso inédito do BossaNova na classificação de melanoma. Isso abre oportunidades de exploração deste descritor em outros contextos médicosAbstract: Melanoma is the type of skin cancer that most leads to death, even being the most curable, if detected early. Since the presence of a full time dermatologist is not economical feasible for many small cities and specially in underserved communities, computer-aided diagnosis for melanoma screening has been a topic of active research. Much of the existing art is based on the Bag-of-Visual-Words (BoVW) model, combining color and texture descriptors. However, the BoVW model has been improving and nowadays there are several extensions that perform better classification rates in general image classification tasks. These enhanced models were not explored yet for melanoma screening, thus motivating our work. Here we present a new approach for melanoma screening, based upon the state-of-the-art BossaNova descriptors, showing very promising results for screening, reaching an AUC of up to 93.7%. This work also proposes a new spatial pooling strategy specially designed for melanoma screening. Other contribution of this research is the unprecedented use of BossaNova in melanoma classification. This opens the opportunity to explore this enhanced mid-level descriptors in other medical contextsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Melanoma Detection Using Mobile Technology and Feature-Based Classification Techniques

Melanoma is one of the most dangerous types of skin cancer in terms of the ratio of death cases. Probability of death increases when it is diagnosed late. However, it is possible to treat melanoma successfully when diagnosed in its early stages. One of the most common medical methods for diagnosing melanoma is the ABCD (Asymmetry, Border irregularity, Color, and Diameter) method that involves the measurement of four features of skin lesions. The main disadvantage of this method is that estimation error and subjectivity affects the accuracy of diagnosis, especially when performed by non-specialists. Scarcity of specialists makes the problem worse. This has led to the development of computer systems to help in melanoma diagnosis. However, while most computer systems can achieve high accuracy with adequate speed, they have problems in the usability and flexibility. The emergence of smart phones with increasing image capture and processing capabilities has made it more possible to use such devices to perform medical image analysis such as the diagnosis of melanoma. Our research work combines existing melanoma diagnosis method and the image capture and processing capabilities of smart phones to achieve fast, affordable, easily available and highly accurate melanoma diagnosis. In this work, we propose a complete smart phone application to capture, and process an image of the suspicious region of the skin in order to estimate its probability of being melanoma. The system can use historical cases to improve its diagnosis accuracy. The system was tested on 164 sample images. 14 images were not well-captured and could not be diagnosed, while the remaining 150 cases were successfully processed. In each of these 150 images, the lesion was correctly segmented and their ABCD feature set extracted. Diagnosis accuracy of the analyzed images ranged between 88%-94 with best results using SVM classifier, and worst is the KNN classifier.الميلانوما أحد أخطر أنواع سرطان الجلد من حيث نسبة عدد الوفيات الى حالات الاصابة ،تزداد الخطورة في الحالات التي يتم معالجتها في مراحل متأخرة ، ولكن يمكن علاج الميلانوما بنجاح اذا تم اكتشاف المرض في مراحله الاولى ، لذلك هناك العديد من الطرق المتبعة لتشخيص المرض في المراحل الأولى لتوجيه المريض الى الطبيب المختص مباشرة في حالة الشك في وجود المرض ، اشهر هذه الطرق هي طريقة ABCD، وأهم العقبات التي تواجه هذه الطريقة هي عدم دقة التنفيذ من الاشخاص غير المتخصصين ، لأن اعتماد الطريقة على خصائص مثل الحجم واللون والشكل يجعلها عرضة للكثير من التقدير والنسبية في التشخيص مما يفقد هذه الطريقة الكثير من الدقة في النتائج ، لذلك تم خلال الاعوام السابقة العمل على العديد من الانظمة المحوسبة التي تسعي الى المساعدة في التشخيص وتقليل نسبة الخطأ، وأتمتة التشخيص بحيث لا يخضع للتقدير الشخصي ، ومع بداية ظهور الاجهزة الذكية والتي تجاوز استخدامها حدود التواصل ليتم استخدامها في التقاط الصور بدقة عالية ومعالجة البيانات بكفاءة والتواصل مع شبكة الانترنت، كان اتجاه للاستفادة من مرونة الأجهزة الذكية ودقتها في التقاط الصور ومعالجتها من اجل توفير انظمة لتشخيص الأمراض المختلفة، وكان نصيب من هذه الابحاث والتطبيقات لتسهيل عملية تشخيص الميلانوما، ورغم توفر العديد من الأنظمة والابحاث الا أن النتائج لا تزال في المراحل الاولى حيث هناك العديد من العقبات، فلا تزال دقة هذه الانظمة لا تصل الى المستوى المطلوب مما يجعلها في بعض الحالات خطر على حياة المريض اذا تم تشخيص الحالات المصابة بشكل سلبي، كما ان امكانات معالجة الصور وتخزين البيانات وتصنيفها يعتبر من المجالات الجديدة والتي لم يتم اختبارها بشكل كافي في الابحاث السابقة ولم يتم التعامل مع الامكانات الجديدة للهواتف الذكية ، وخلال فترة قريبة كان مثلا تحليل كميات كبيرة من البيانات على الهواتف الذكية ومعالجتها وتصنيفها من الامور الغير ممكنة والتي اصبحت الان من الامور الممكنة في ظل تطور وحدات التخزين والمعالجة بشكل كبير للهواتف الذكية. لذلك جاء هذا العمل لمواصلة الجهد المبذول في توفير حل لمشكلة تشخيص مرض الميلانوما بشكل دقيق وفعال ومرن باستخدام الهواتف الذكية ، حيث تم استخدام امكانات الهواتف الذكية في التقاط الصور ومعالجتها بالإضافة لإمكانية تخزين المعلومات واستخدامها في التصنيف وتوقع الحالات الجديدة المصابة بالمرض، وتم بناء واختبار نظام كامل لتحقيق ذلك ، وقد كانت نتائج العمل مُرضِية جدا حيث تم فحص البرنامج على عينة مكونة من 164 صورة حيث نجح البرنامج في مرحلة معالجة الصورة في معالجة 150 صورة وعزل منطقة الآفة من أصل 164 صورة كما ذكرنا، علما ان نسبة النجاح في معالجة الصور تم تحسينها لتجاوز الأخطاء اثناء التقاط الصور من خلال استخدام واجهة تفاعلية للمستخدم ، اما في مرحلة التصنيف علي 150 صورة الناتجة من معالجة الصور فكانت دقة نتائج التصنيف بين 88-94% حسب نظام التصنيف المتبع ، وهي نتائج جيدة و يمكن البناء عليها في استخدام الموبايل في التشخيص الأولى لمرضى الميلانوم

Multimedia sensors embedded in smartphones for ambient assisted living and e-health

The final publication is available at link.springer.com[EN] Nowadays, it is widely extended the use of smartphones to make human life more comfortable. Moreover, there is a special interest on Ambient Assisted Living (AAL) and e-Health applications. The sensor technology is growing and amount of embedded sensors in the smartphones can be very useful for AAL and e-Health. While some sensors like the accelerometer, gyroscope or light sensor are very used in applications such as motion detection or light meter, there are other ones, like the microphone and camera which can be used as multimedia sensors. This paper reviews the published papers focused on showing proposals, designs and deployments of that make use of multimedia sensors for AAL and e-health. We have classified them as a function of their main use. They are the sound gathered by the microphone and image recorded by the camera. 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Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis. © 2016 Optical Society of America.1

Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

Background Melanoma accounts for a small proportion of all skin cancer cases but is responsible for the majority of skin cancer-related deaths. Early detection and treatment can improve survival. Smartphone applications are readily accessible and potentially offer an instant risk assessment of the likelihood of malignancy, so that the right people seek further medical attention from a clinician for more detailed assessment of the lesion. There is, however, a risk that melanomas will be missed and treatment delayed if the application reassures the user that their lesion is low risk. Objectives To determine the diagnostic accuracy of smartphone applications to rule out cutaneous invasive melanoma and intraepidermal melanocytic variants in adults with concerns about suspicious skin lesions.NIH

Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules.

OBJECTIVES: Malignant melanoma has high morbidity and mortality rates. Early diagnosis improves prognosis. Clinical prediction rules (CPRs) can be used to stratify patients with symptoms of suspected malignant melanoma to improve early diagnosis. We conducted a systematic review of CPRs for melanoma diagnosis in ambulatory care. DESIGN: Systematic review. DATA SOURCES: A comprehensive search of PubMed, EMBASE, PROSPERO, CINAHL, the Cochrane Library and SCOPUS was conducted in May 2015, using combinations of keywords and medical subject headings (MeSH) terms. STUDY SELECTION AND DATA EXTRACTION: Studies deriving and validating, validating or assessing the impact of a CPR for predicting melanoma diagnosis in ambulatory care were included. Data extraction and methodological quality assessment were guided by the CHARMS checklist. RESULTS: From 16 334 studies reviewed, 51 were included, validating the performance of 24 unique CPRs. Three impact analysis studies were identified. Five studies were set in primary care. The most commonly evaluated CPRs were the ABCD, more than one or uneven distribution of Colour, or a large (greater than 6 mm) Diameter (ABCD) dermoscopy rule (at a cut-point of \u3e4.75; 8 studies; pooled sensitivity 0.85, 95% CI 0.73 to 0.93, specificity 0.72, 95% CI 0.65 to 0.78) and the 7-point dermoscopy checklist (at a cut-point of ≥1 recommending ruling in melanoma; 11 studies; pooled sensitivity 0.77, 95% CI 0.61 to 0.88, specificity 0.80, 95% CI 0.59 to 0.92). The methodological quality of studies varied. CONCLUSIONS: At their recommended cut-points, the ABCD dermoscopy rule is more useful for ruling out melanoma than the 7-point dermoscopy checklist. A focus on impact analysis will help translate melanoma risk prediction rules into useful tools for clinical practice

Системы анализа биомедицинских данных для диагностики злокачественных новообразований кожи

Introduction. The effectiveness of the diagnosis of malignant neoplasms of the skin remains unsatisfactory due to the complex process of interpretation of clinical features. On the other hand, in the last two decades, noninvasive optical diagnostic methods have been actively developed, for example, digital dermatoscopy for visualization of surface neoplasms and Optical Coherence Tomography (OCT) for obtaining spatial scans. Recent advances in the study of non-invasive diagnostic tools makes this area very promising for research in a clinical condition. Aim. Developing of software modules based on the mathematical framework of texture analysis for biomedical data systems designed for the diagnosis of skin malignant neoplasms. Materials and methods. Algorithms of software modules developed for optical systems of our own design are presented. Algorithms for a dermatoscopic module are based on the Haar transform, Local Binary Patterns and color features. Algorithms for OCT are based on the texture features of Haralick, Tamura, fractal dimension, complex directional field and Markov random field. Studies were conducted on sets of 106 dermatoscopic and 1008 OCT images of various classes of pathologies, including melanoma and Basal Cell Carcinoma (BCC). Results. The values of sensitivity and specificity for the dermatoscopic system and OCT were experimentally obtained. Conclusion. The sensitivity of the dermatoscopic system is 90 % versus 93 % for other authors, as well as the specificity is 86 % versus 80 %. One of the factors of the increase can be considered the introduction of a personalized mode - the addition of comparative features evaluating a difference between a tumor and a normal tissue in the software analysis module. The improved accuracy of OCT is up to 97 % for the diagnosis of melanoma and up to 96 % for the diagnosis of BCC.Введение. Эффективность диагностики злокачественных новообразований кожи остается неудовлетворительной ввиду сложного процесса интерпретации клинических признаков. С другой стороны, в последние два десятилетия активно развиваются неинвазивные оптические методы диагностики, например цифровая дерматоскопия для визуализации поверхностных новообразований и оптическая когерентная томография (ОКТ) для получения пространственных срезов. Последние успехи в области исследований неинвазивных средств диагностики делают данную область весьма перспективной для исследований в клинических условиях. Цель работы. Создание программных модулей на основе математического аппарата текстурного анализа для биомедицинских систем, предназначенных для диагностики злокачественных новообразований кожи. Материалы и методы. Представлены алгоритмы программных модулей, созданных для оптических установок собственной разработки. Программные модули для дерматоскопического модуля выполнены на основе преобразования Хаара, локальных бинарных шаблонов и цветовых признаков, а для ОКТ - на базе признаков Харалика, Тамура, фрактальной размерности, комплексного поля направлений и марковских случайных полей. Проведены исследования на наборах из 106 дерматоскопических и 1008 ОКТ-изображений, содержащих различные классы патологий, включая меланому и базально-клеточную карциному (БКК). Результаты. Экспериментально получены значения чувствительности и специфичности для дерматоскопической системы и ОКТ. Заключение. Чувствительность дерматоскопической системы с разработанными алгоритмами составила 90 против 93 % по известным источникам, специфичность - 86 против 80 %. Одним из факторов увеличения можно считать введение персонифицированного режима – добавление сравнительных признаков, оценивающих различия между опухолью и нормальной тканью, в программный модуль анализа. При диагностике меланомы точность ОКТ повышена до 97 %, а при диагностике БКК – до 96 %

Effectiveness of interventions to support the early detection of skin cancer through skin self-examination: a systematic review and meta-analysis.

BACKGROUND: As skin cancer incidence rises, there is a need to evaluate early detection interventions by the public using skin self-examination (SSE); however, the literature focuses on primary prevention. No systematic reviews have evaluated the effectiveness of such SSE interventions. OBJECTIVES: To systematically examine, map, appraise and synthesize, qualitatively and quantitatively, studies evaluating the early detection of skin cancer, using SSE interventions. METHODS: This is a systematic review (narrative synthesis and meta-analysis) examining randomized controlled trials (RCTs) and quasiexperimental, observational and qualitative studies, published in English, using PRISMA and National Institute for Health and Care Excellence guidance. The MEDLINE, Embase and PsycINFO databases were searched through to April 2015 (updated in April 2018 using MEDLINE). Risk-of-bias assessment was conducted. RESULTS: Included studies (n = 18), totalling 6836 participants, were derived from 22 papers; these included 12 RCTs and five quasiexperiments and one complex-intervention development. More studies (n = 10) focused on targeting high-risk groups (surveillance) than those at no higher risk (screening) (n = 8). Ten (45%) study interventions were theoretically underpinned. All of the study outcomes were self-reported, behaviour related and nonclinical in nature. Meta-analysis demonstrated the impact of the intervention on the degree of SSE activity from five studies, especially in the short term (up to 4 months) (odds ratio 2·31, 95% confidence interval 1·90-2·82), but with small effect sizes. Risk-of-bias assessment indicated that 61% of the studies (n = 11) were of weak quality. CONCLUSIONS: Four RCTs and a quasiexperimental study indicate that some interventions can enhance SSE activity and so are more likely to aid early detection of skin cancer. However, the actual clinical impact remains unclear, and this is based on overall weak study (evidence) quality

Esquemas de transferência para aprendizado profundo em classificação de imagens

Orientadores: Eduardo Alves do Valle Junior, Sandra Eliza Fontes de AvilaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Em Visão Computacional, a tarefa de classificação é complexa, pois visa a detecção da presença de categorias em imagens, dependendo criticamente da habilidade de aprender modelos computacionais generalistas a partir de amostras de treinamento. Aprendizado Profundo (AP) para tarefas visuais geralmente envolve o aprendizado de todos os passos deste processo, da extração de características até a atribuição de rótulos. Este tipo pervasivo de aprendizado garante aos modelos de AP maior capacidade de generalização, mas também traz novos desafios: um modelo de AP deverá estimar um grande número de parâmetros, exigindo um imenso conjunto de dados anotados e grandes quantidades de recursos computacionais. Neste contexto, a Transferência de Aprendizado emerge como uma solução promissora, permitindo a reciclagem de parâmetros aprendidos por modelos diferentes. Motivados pela crescente quantidade de evidências para o potencial de tais técnicas, estudamos de maneira abrangente a transferência de conhecimento de arquiteturas profundas aplicada ao reconhecimento de imagens. Nossos experimentos foram desenvolvidos para explorar representações internas de uma arquitetura profunda, testando sua robustez, redundância e precisão, com aplicações nos problemas de rastreio automático de melanoma, reconhecimento de cenas (MIT Indoors) e detecção de objetos (Pascal VOC). Também levamos a transferência a extremos, introduzindo a Transferência de Aprendizado Completa, que preserva a maior parte do modelo original, mostrando que esquemas agressivos de transferência podem atingir resultados competitivosAbstract: In Computer Vision, the task of classification is complex, as it aims to identify the presence of high-level categories in images, depending critically upon learning general models from a set of training samples. Deep Learning (DL) for visual tasks usually involves seamlessly learning every step of this process, from feature extraction to label assignment. This pervasive learning improves DL generalization abilities, but brings its own challenges: a DL model will have a huge number of parameters to estimate, thus requiring large amounts of annotated data and computational resources. In this context, transfer learning emerges as a promising solution, allowing one to recycle parameters learned among different models. Motivated by the growing amount of evidence for the potential of such techniques, we study transfer learning for deep architectures applied to image recognition. Our experiments are designed to explore the internal representations of DL architectures, testing their robustness, redundancy and precision, with applications to the problems of automated melanoma screening, scene recognition (MIT Indoors) and object detection (Pascal VOC). We also take transfer learning to extremes, introducing Complete Transfer Learning, which preserves most of the original model, showing that aggressive transfer schemes can reach competitive resultsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric