3,875 research outputs found

    Multispectral imaging methods for the diagnosis of skin cancer lesions

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    En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB).Skin cancer is the most prevalent form of cancer, and melanoma is one of the most threat disease of it. But it can be cured if it is detected early enough. Multispectral imaging is a potential method to differenciate melanoma from nevi as it provides spectral images with information of absorbance and reflectance. With this aim, spectral images along the visible and near infrared range (from 415nm to 995nm) of 165 lesions including nevi, melanomas and basal cell carcinomas were processed in this master thesis. After obtaining all data in terms of reflectance and absorbance and other related parameters for each pixel of the segmented lesions, a statistical analysis was carried out to quantify their spatial distribution all over each lesion. Algorithms such as Support vector machine (SVM) and Discriminant Analysis (DA) were used as a means of classifying the lesions. The results show that DA linear classifier provides a better diagnosis than the SVM. BCCs are easier to discriminate from nevi than melanomas

    Machine learning methods for histopathological image analysis

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    Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

    Serial Dependence in Dermatological Judgments

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    This research was funded by the National Institutes of Health (NIH) grant number R01CA236793.Peer reviewedPublisher PD

    Skin Cancer Prevention and Screening in the Republic of Macedonia

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    Skin cancer is among the most common types of cancer. The incidence of skin cancer is very high and raising worldwide, especially its most serious and aggressive form, melanoma. Severe childhood sunburn and long term sun exposure over many years are the leading risk factors for skin cancer. Macedonia is situated in the region with high UV index of radiation and has an average annual incidence of skin cancer, compared with other countries in Europe and in the world. Almost all skin cancers are preventable and they are highly curable if detected and treated early. Even malignant melanoma is almost 100 percent curable if detected early, before the cancer has invaded into the deeper layers of the skin. Since the year 2005 a new technology, dermatoscopy, has been introduced in the routine practice at the Clinic of Dermatology at the Clinical Center in Skopje. This new method of skin cancer detection makes possible diagnosis of melanoma in in situ stadium. It was the starting point for multiple activities and programs, which the Clinic has undertaken within the last two years, for prevention, screening and early detection of the skin cancer, as well as managing further appropriate cure. A new dermato-oncology unit was founded within the Clinic of Dermatology. Currently it is engaged in: clinical protocols of diagnosis and prevention, educational activities and informative campaigns, education of medical staff for working with this category of patients, selection of patients into groups (patients with low and high risk for developing skin cancer) and their followup in regular intervals, creating medical records in electronic form for each patient, promotion of dermatoscopy as a method, use of modern information and communication technologies (telemedicine, teledermatology) and active participation in the international activities for skin cancer control

    Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images

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    Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret’s diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified

    Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis

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    This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector
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