Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Aerospace Medicine and Biology: A continuing bibliography (supplement 229)

This bibliography lists 109 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1982

Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma

Melanoma is the most widely occurring and life threatening form of skin cancer. Early detection of in situ melanoma has challenged researchers for many decades now. Currently there exists no computer aided mechanisms to accurately detect early melanoma. T he currently existing computer aided diagnostics mechanisms are capable of melanoma classification and are unable to detect in situ melanoma. This paper introduces a Multi Parameter Extraction and Classification System ( 푀푀푀푀푀 ) to aid early detection o f skin cancer melanoma. The 푀푀푀푀푀 defines the skin lesion images in terms of characteristic parameters which are further used for classification. In this paper the extraction of 21 parameters is achieved using a six phase approach. The parameters extr acted are analyzed using statistical methods. It is clear from the results obtained that no single parameter can affirm the detection of in situ melanoma, hence an advanced analysis mechanisms considering all the parameters need to be adopted to effective ly detect melanoma in its initial stages

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits

Review on automatic early skin cancer detection

Skin cancer is increasing in different countries especially in Australia. Early detection of skin cancer can treat melanoma successfully, therefore, curability and survival depends directly on removing melanoma in its early stages. Since clinical observations face to different fault for melanoma detection, the automatic diagnosis can help to increase the accuracy of detection. Reviewing the researches have done in skin cancer detection and providing the overview on automatic detection of skin cancer are the ultimate aims of this paper. It presents the literature on automatic skin cancer detection and describes the different steps of such process. © 2011 IEEE

The beneficial techniques in preprocessing step of skin cancer detection system comparing

© 2014 The Authors. Automatic diagnostics of skin cancer is one of the most challenging problems in medical image processing. It helps physicians to decide whether a skin melanoma is benign or malignant. So, determining the more efficient methods of detection to reduce the rate of errors is a vital issue among researchers. Preprocessing is the first stage of detection to improve the quality of images, removing the irrelevant noises and unwanted parts in the background of the skin images. The purpose of this paper is to gather the preprocessing approaches can be used in skin cancer images. This paper provides good starting for researchers in their automatic skin cancer detections

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

GŁĘBOKIE SIECI NEURONOWE DLA DIAGNOSTYKI ZMIAN SKÓRNYCH

Non-invasive diagnosis of skin cancer is extremely necessary. In recent years, deep neural networks and transfer learning have been very popular in the diagnosis of skin diseases. The article contains selected basics of deep neural networks, their interesting applications created in recent years, allowing the classification of skin lesions from available dermatoscopic images.Nieinwazyjna diagnostyka nowotworów skóry jest niezwykle potrzebna. W ostatnich latach bardzo dużym zainteresowaniem w diagnostyce chorób skóry cieszą się głębokie sieci neuronowe i transfer learning. Artykuł zawiera wybrane podstawy głębokich sieci neuronowych, ich ciekawe zastosowania stworzone w ostatnich latach, pozwalające na klasyfikację zmian skórnych z dostępnych obrazów dermatoskopowych