Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters
暂无分享,去创建一个
Skaidra Valiukevičienė | Kumar Anubhav Tiwari | Renaldas Raišutis | Jokūbas Liutkus | R. Raišutis | S. Valiukevičienė | Jokūbas Liutkus
[1] C. Longo,et al. Cost–benefit of reflectance confocal microscopy in the diagnostic performance of melanoma , 2016, Journal of the European Academy of Dermatology and Venereology : JEADV.
[2] Tim Holland-Letz,et al. Pathologist-level classification of histopathological melanoma images with deep neural networks. , 2019, European journal of cancer.
[3] Jie Tian,et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma , 2018, European Radiology.
[4] D M Parkin,et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods , 2018, International journal of cancer.
[5] D. Silvers,et al. Dermatopathology standards , 1996, Journal of cutaneous pathology.
[6] R. Raišutis,et al. Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images , 2018, The Libyan journal of medicine.
[7] T. Johnson. Guidelines of care for the management of primary cutaneous melanoma. , 2013, Journal of the American Academy of Dermatology.
[8] Joanna Jaworek-Korjakowska,et al. Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines , 2016, BioMed research international.
[9] E. Farmer,et al. Discordance in the histopathologic diagnosis of melanoma and melanocytic nevi between expert pathologists. , 1996, Human pathology.
[10] Samy Bakheet,et al. An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features , 2017, Comput..
[11] A. Rossi,et al. Emerging imaging technologies in dermatology: Part II: Applications and limitations , 2019, Journal of the American Academy of Dermatology.
[12] H. Williams,et al. Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. , 2018, The Cochrane database of systematic reviews.
[13] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[14] Jinhua Yu,et al. Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images , 2018, BMC Cancer.
[15] Joann G Elmore,et al. Pathologists ' diagnosis of invasive melanoma and melanocytic proliferations : observer accuracy and reproducibility study , 2019 .
[16] Philippe Lambin,et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures , 2017, The British journal of radiology.
[17] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[18] R Kazys,et al. Principles of high‐frequency ultrasonography for investigation of skin pathology , 2011, Journal of the European Academy of Dermatology and Venereology : JEADV.
[19] D. Grossman,et al. Current controversies in early-stage melanoma: Questions on incidence, screening, and histologic regression. , 2019, Journal of the American Academy of Dermatology.
[20] Eman Shaheen,et al. Enhancement of 3D modeling and classification of microcalcifications in breast computed tomography (BCT) , 2014, Medical Imaging.
[21] A. Gavin,et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. , 2018, European journal of cancer.
[22] D SeejaR,et al. Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM) , 2019 .
[23] Anjali Dhall,et al. Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients , 2018, Scientific Reports.
[24] Yi Guo,et al. Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma , 2017, Clinical breast cancer.
[25] Achim Hekler,et al. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. , 2019, European journal of cancer.
[26] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[27] El-Sayed M. El-Horbaty,et al. Malignant Melanoma Detection Based on Machine Learning Techniques : A Survey 1 , 2016 .
[28] Jeffrey E Gershenwald,et al. Melanoma staging: Evidence‐based changes in the American Joint Committee on Cancer eighth edition cancer staging manual , 2017, CA: a cancer journal for clinicians.
[29] R. Ferrini. Screening for skin cancer. , 2002, American family physician.
[30] J. Emery,et al. Effect of adding a diagnostic aid to best practice to manage suspicious pigmented lesions in primary care: randomised controlled trial , 2012, BMJ : British Medical Journal.
[31] Sotiris B. Kotsiantis,et al. Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.
[32] Seeja R D,et al. Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM) , 2019, Asian Pacific journal of cancer prevention : APJCP.
[33] A. Cesinaro,et al. Impact of Dermoscopy and Reflectance Confocal Microscopy on the Histopathologic Diagnosis of Lentigo Maligna/Lentigo Maligna Melanoma , 2018, The American Journal of dermatopathology.
[34] M. Niranjan. Support vector machines: a tutorial overview and critical appraisal , 1999 .
[35] A. Rossi,et al. Emerging imaging technologies in dermatology: Part I: Basic principles , 2018, Journal of the American Academy of Dermatology.
[36] Farid García,et al. A comprehensive survey on support vector machine classification: Applications, challenges and trends , 2020, Neurocomputing.
[37] Gintarė Linkevičiūtė,et al. Automatic Differential Diagnosis of Melanocytic Skin Tumors Using Ultrasound Data. , 2016, Ultrasound in medicine & biology.
[38] Tsuyoshi Nakamura,et al. Survey on Neural Networks Used for Medical Image Processing. , 2009, International journal of computational science.
[39] F. Patat,et al. One-step surgical removal of cutaneous melanoma with surgical margins based on preoperative ultrasound measurement of the thickness of the melanoma , 2018, European Journal of Dermatology.
[40] Tumaini R. Coker,et al. Screening for Skin Cancer: US Preventive Services Task Force Recommendation Statement. , 2023, JAMA.
[41] Daniel L. Rubin,et al. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images , 2018, Journal of Digital Imaging.
[42] R. Manmatha,et al. Chapter 10 – Shape-Based Image Classification and Retrieval , 2013 .
[43] G J Mann,et al. Clinical practice guidelines for identification, screening and follow‐up of individuals at high risk of primary cutaneous melanoma: a systematic review , 2015, The British journal of dermatology.
[44] H. Haenssle,et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.
[45] R. Raišutis,et al. Automated Estimation of Melanocytic Skin Tumor Thickness by Ultrasonic Radiofrequency Data , 2016, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.
[46] I Zalaudek,et al. Meta‐analysis of digital dermoscopy follow‐up of melanocytic skin lesions: a study on behalf of the International Dermoscopy Society , 2013, Journal of the European Academy of Dermatology and Venereology : JEADV.
[47] Xiaoyu Cui,et al. Assessing the effectiveness of artificial intelligence methods for melanoma:A retrospective review. , 2019, Journal of the American Academy of Dermatology.
[48] Meritxell Vilaseca,et al. Visible and Extended Near-Infrared Multispectral Imaging for Skin Cancer Diagnosis , 2018, Sensors.
[49] Chengjun Liu,et al. Discriminant analysis and similarity measure , 2014, Pattern Recognit..
[50] K. P. Sanal Kumar,et al. Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers , 2019, Journal of Medical Systems.
[51] Achim Hekler,et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. , 2019, European journal of cancer.
[52] R. Bard,et al. Ultrasound in dermatology: principles and applications. , 2012, Journal of the American Academy of Dermatology.
[53] The significance of spectrophotometric image analysis for diagnosis of the melanocytic skin tumours in association with their thickness , 2018, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[54] H. Williams,et al. High-frequency ultrasound for diagnosing skin cancer in adults. , 2018, The Cochrane database of systematic reviews.
[55] A. Thrift,et al. A Model to Predict the Risk of Keratinocyte Carcinomas. , 2016, The Journal of investigative dermatology.
[56] Liangxiao Jiang,et al. A Novel Bayes Model: Hidden Naive Bayes , 2009, IEEE Transactions on Knowledge and Data Engineering.
[57] C. Compton,et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging , 2017, CA: a cancer journal for clinicians.
[58] S. Menzies,et al. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta‐analysis of studies performed in a clinical setting , 2008, The British journal of dermatology.
[59] P. Tschandl,et al. Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. , 2019, JAMA dermatology.
[60] Ali Mohammad Alqudah,et al. The melanoma skin cancer detection and classification using support vector machine , 2017, 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).
[61] Mayura Patil,et al. Melanoma Detection Using HSV with SVM Classifier and De-duplication Technique to Increase Efficiency , 2020 .