Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma: A Web-Based International Dermoscopy Society Study.

IMPORTANCE: The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied. OBJECTIVES: To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets. MAIN OUTCOMES AND MEASURES: Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: the ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity). RESULTS: A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95% CI, 5.6-7.8), pattern asymmetry (OR, 4.9; 95% CI, 4.1-5.8), nonorganized pattern (OR, 3.3; 95% CI, 2.9-3.7), border score of 6 (OR, 3.3; 95% CI, 2.5-4.3), and contour asymmetry (OR, 3.2; 95% CI, 2.7-3.7) (P < .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95% CI, 0.40-0.49), absence of vessels (ICC, 0.46; 95% CI, 0.42-0.51), dark brown color (ICC, 0.40; 95% CI, 0.35-0.44), and architectural disorder (ICC, 0.43; 95% CI, 0.39-0.48). The Menzies method had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P < .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves. CONCLUSIONS AND RELEVANCE: Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor

Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma aweb-based international dermoscopy society study

IMPORTANCE The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied. OBJECTIVES To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms. DESIGN, SETTING, AND PARTICIPANTS Thiswas a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-Type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets. MAIN OUTCOMES AND MEASURES Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: The ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity). RESULTS A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95%CI, 5.6-7.8), pattern asymmetry (OR, 4.9; 95%CI, 4.1-5.8), nonorganized pattern (OR, 3.3; 95%CI, 2.9-3.7), border score of 6 (OR, 3.3; 95%CI, 2.5-4.3), and contour asymmetry (OR, 3.2; 95%CI, 2.7-3.7) (P &lt; .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95%CI, 0.40-0.49), absence of vessels (ICC, 0.46; 95%CI, 0.42-0.51), dark brown color (ICC, 0.40; 95%CI, 0.35-0.44), and architectural disorder (ICC, 0.43; 95%CI, 0.39-0.48). The Menziesmethod had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P &lt; .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves. CONCLUSIONS AND RELEVANCE Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor

Dermoscopic evaluation of nodular melanoma

Importance: Nodular melanoma (NM) is a rapidly progressing potentially lethal skin tumor for which early diagnosis is critical

Accurate segmentation and registration of skin lesion images to evaluate lesion change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

In recent years, many attempts have been dedicated to the creation of automated devices that could assist both expert and beginner dermatologists towards fast and early diagnosis of skin lesions. Tasks such as skin lesion classification and segmentation have been extensively addressed with deep learning algorithms, which in some cases reach a diagnostic accuracy comparable to that of expert physicians. However, the general lack of interpretability and reliability severely hinders the ability of those approaches to actually support dermatologists in the diagnosis process. In this paper a novel skin image retrieval system is presented, which exploits features extracted by Convolutional Neural Networks to gather similar images from a publicly available dataset, in order to assist the diagnosis process of both expert and novice practitioners. In the proposed framework, ResNet-50 is initially trained for the classification of dermoscopic images; then, the feature extraction part is isolated, and an embedding network is built on top of it. The embedding learns an alternative representation, which allows to check image similarity by means of a distance measure. Experimental results reveal that the proposed method is able to select meaningful images, which can effectively boost the classification accuracy of human dermatologists

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

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

The role of AI classifiers in skin cancer images

Background: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods: The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results: The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion: Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.info:eu-repo/semantics/publishedVersio