IEEE Organizations related to Dermatology

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Conferences related to Dermatology

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


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Periodicals related to Dermatology

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Most published Xplore authors for Dermatology

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Xplore Articles related to Dermatology

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On Extraction of Rules from Deep Learner: The Deeper, the Better?

2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2017

In recent years, multilayer perceptron (MLP) has been successfully used for solving various problems in different fields. However, it is difficult to interpret the reasoning process of an MLP, and therefore in most cases the MLP is used as a black box. In our previous study, we tried to extract rules from a learned shallow MLP based on the hidden ...


Utility of polarized dermoscopy in the diagnosis of cutaneous lupus erythematosus and morphea

2017 E-Health and Bioengineering Conference (EHB), 2017

Dermoscopy is a non-invasive in vivo technique to help visualize morphological structures on the surface and subsurface of the skin, non-visible with the naked eye. It is tipically used to improve the diagnostic accuracy of pigmented skin lesions and skin tumors, but in recent years its use has been extended to other dermatological conditions. The use of polarized light in ...


Classification of skin lesions using ANN

2016 Medical Technologies National Congress (TIPTEKNO), 2016

Melanoma arises from cancerous growth in pigmented skin lesion and t is the most deadliest form of skin cancer. Melanoma forms 4% from all skin cancer cases and it accounts for 75% of all skin cancer deaths. Even when the expert dermatologists uses the dermoscopy for diagnosis, the accuracy of melanoma diagnosis is estimated to be about 75-84%. The aim ...


Choose of wart treatment method using Naive Bayes and k-nearest neighbors classifiers

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018

In this study, the success of cyrotheraphy and immunotherapy methods on common warts and plantar warts were predicted among 180 patients using machine learning methods. As a classifier, Naive Bayes and k-nearest neighbors with different neighborhood values of k were experimented. Data sets that are online available via Internet were used in the study. As a result, whether the treatment ...


An Evolutionary Based Multi-Objective Filter Approach for Feature Selection

2017 World Congress on Computing and Communication Technologies (WCCCT), 2017

Feature selection is one of the important research areas in pattern recognition. The aim of feature selection is to select those of informative features to improve the classifier's performance. In this paper, we propose a novel multi-objective algorithm based on mutual information for feature selection, called multi-objective mutual information (MOMI). The proposed method identifies a set of features with minimal ...


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Educational Resources on Dermatology

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IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Dermatology"

IEEE-USA E-Books

  • On Extraction of Rules from Deep Learner: The Deeper, the Better?

    In recent years, multilayer perceptron (MLP) has been successfully used for solving various problems in different fields. However, it is difficult to interpret the reasoning process of an MLP, and therefore in most cases the MLP is used as a black box. In our previous study, we tried to extract rules from a learned shallow MLP based on the hidden neuron outputs. In this study, we investigate the possibility of extracting simpler and better rules from a deep MLP. It is believed that hidden layers closer to the output layer can learn more abstract concepts. It is natural to expect that simpler and better rules can be extracted from higher layers. Experimental results on several public datasets reveal that this is true because the decision trees designed based on hidden layers closer to the output layer are actually smaller. That is, it is possible to extract more understandable knowledge from a deep MLP, even if the MLP as a whole is difficult to understand. In addition, based on the complexity of the extracted knowledge, it is also possible to determine the number of layers needed for solving a given problem.

  • Utility of polarized dermoscopy in the diagnosis of cutaneous lupus erythematosus and morphea

    Dermoscopy is a non-invasive in vivo technique to help visualize morphological structures on the surface and subsurface of the skin, non-visible with the naked eye. It is tipically used to improve the diagnostic accuracy of pigmented skin lesions and skin tumors, but in recent years its use has been extended to other dermatological conditions. The use of polarized light in dermoscopy allows a better visualization of vascular structures and other changes in the dermis such as collagen depositin and fibrosis, which would make it a good tool for clinical orientation in inflammatory conditions such as cutaneous lupus erythematosus and morphea, considered in the spectrum of autoimmune connective tissue diseases, with diverse clinical cutaneous manifestations. Up to date studies of dermoscopic clues in these two entities are still scarce and results are not uniform. We performed a retrospective analysis of dermoscopic images in patients with pathological confirmation of the clinical diagnosis in order to find the common features and clues for dermoscopic diagnosis.

  • Classification of skin lesions using ANN

    Melanoma arises from cancerous growth in pigmented skin lesion and t is the most deadliest form of skin cancer. Melanoma forms 4% from all skin cancer cases and it accounts for 75% of all skin cancer deaths. Even when the expert dermatologists uses the dermoscopy for diagnosis, the accuracy of melanoma diagnosis is estimated to be about 75-84%. The aim of this work classify skin lesions like normally, atypical and melanoma using artificial intelligence techniques and help to decide of the expert dermatologists in diagnosis for melanoma. Decision support system, which will be held improve both the speed and the accuracy of diagnosis. In this study that done for the classification of skin lesions with ANN, were correctly classified 100% normal skin lesions according to data from the data set PH2. Abnormal and melanoma skin cancers are correctly classified %93.3 with neural network that performed. As a result, the findings that were obtained have indicated the decision support system will be help to the dermatologists in the diagnosis of skin lesions.

  • Choose of wart treatment method using Naive Bayes and k-nearest neighbors classifiers

    In this study, the success of cyrotheraphy and immunotherapy methods on common warts and plantar warts were predicted among 180 patients using machine learning methods. As a classifier, Naive Bayes and k-nearest neighbors with different neighborhood values of k were experimented. Data sets that are online available via Internet were used in the study. As a result, whether the treatment method by considering given features will give positive result could be estimated with the accuracy of 80% by using k-nearest neighbors classifier with the neighborhood value of k=7.

  • An Evolutionary Based Multi-Objective Filter Approach for Feature Selection

    Feature selection is one of the important research areas in pattern recognition. The aim of feature selection is to select those of informative features to improve the classifier's performance. In this paper, we propose a novel multi-objective algorithm based on mutual information for feature selection, called multi-objective mutual information (MOMI). The proposed method identifies a set of features with minimal redundancy and maximum relevancy with the target class. Several experiments are performed to evaluate the performance of MOMI compared to that of well-known and state-of-the-art feature selection methods over five benchmark datasets. The results show that in most cases MOMI achieves better classification performance than others.

  • Computer-aided diagnosis of four common cutaneous diseases using deep learning algorithm

    With the emergence of deep-learning algorithms, the accuracy of computer-aided supporting systems advanced., However, their adoption in the field of medicine has been limited, partially due to the challenges of generating reliable and timely results. In this research, we focused on classifying four common cutaneous diseases based on dermoscopic images using deep learning algorithms.

  • Automatic detection of translucency using a deep learning method from patches of clinical basal cell carcinoma images

    Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early stage because the feature can be observed readily in clinical examinations with a high specificity of 93%. Therefore, translucency detection is a critical component of computer aided systems which aim at early detection of basal cell carcinoma. To address this problem, we proposed an automated method for analyzing patches of clinical basal cell carcinoma images using stacked sparse autoencoder (SSAE). SSAE learns high-level features in unsupervised manner and all learned features are fed into a softmax classifier for translucency detection. Across the 4401 patches generated from 32 clinical images, the proposed method achieved a 93% detection accuracy from a five-fold cross- validation. The preliminary result suggested that the proposed method could detect translucency from skin images.

  • Skin Alterations in Pseudoxanthoma Elasticum Patients Highlighted by the Bi-Dimensional Sample Entropy Algorithm

    BACKGROUND: Pseudoxanthoma elasticum (PXE) is a hereditary disease that manifests - among others - with papular lesions on the skin. The presence of these papules is the earliest sign of the pathology. Their detection is therefore of importance but, due to their small size, this can become a difficult task. This is why dermoscopy, a noninvasive imaging modality, may be of interest. Nevertheless, the detection of papules on dermoscopic images is still a challenge. OBJECTIVE: We propose an image processing framework to help in the detection of papules from dermoscopic images. Our algorithm is based on the recently-proposed bi-dimensional sample entropy. METHODS: Seven PXE patients participated to the study. For each of them, one dermoscopic image of the neck (where the papules are predominant), and one dermoscopic image of a normal skin zone have been recorded. For each one, the bi-dimensional sample entropy has been computed. RESULTS: We observed statistically significantly lower bi-dimensional sample entropy values on the dermoscopic images of the neck than on the dermoscopic images of the normal zone. CONCLUSION: These preliminary findings show that the bi-dimensional sample entropy might be of interest for the diagnosis and follow-up of the PXE pathology.

  • The effect of autoencoders over reducing the dimensionality of a dermatology data set

    The effect of using autoencoders for dimensionality reduction of a medical data set is investigated. A stack of two autoencoders has been trained for popular benchmark medical data set for dermatological disease diagnosis. The improvement of the presented approach has been visualized by the Principal Component Analysis method. Results shows that the use of a autoencoders significantly improves the accuracy of dermatological disease diagnosis.

  • High Definition Live Interactive and Store and Forward Teledermatology: A Comparison of Concordance, Confidence, and Satisfaction with In-person Exams

    210 patients were examined in-person by two dermatologists (a resident and attending) and by residents using store and forward and live interactive teledermatology. Store and forward images were 10 megapixel 24 bit color JPEGs. Live interactive video was either compressed H.264 high definition 720p resolution transmitted at 2 mbps or uncompressed high definition 1080i transmitted at 1.5 gbps. Patients were examined 3 times, once by each method in special weekend clinics and the type of video alternated between clinics. Diagnostic concordance and confidence was significantly higher for in-person exams than remote methods. Store and forward and uncompressed video concordance and confidence were about the same and significantly better than compressed video. Patients and dermatologists were significantly more satisfied with in-person consultations than remote ones. Patients were evenly divided on store and forward and live interactive methods. Dermatologists had a slight preference for uncompressed video over store and forward and were uniformly dissatisfied with compressed video.



Standards related to Dermatology

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Jobs related to Dermatology

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