Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval
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Giovanni Pellacani | Federico Bolelli | Stefano Allegretti | Costantino Grana | Federico Pollastri | Sabrina Longhitano | C. Grana | G. Pellacani | F. Pollastri | Federico Bolelli | Stefano Allegretti | S. Longhitano
[1] James X. Sun,et al. Update on BRAF and MEK inhibition for treatment of melanoma in metastatic, unresectable, and adjuvant settings , 2019, Expert opinion on drug safety.
[2] Costantino Grana,et al. Augmenting data with GANs to segment melanoma skin lesions , 2019, Multimedia Tools and Applications.
[3] G Pellacani,et al. Algorithmic reproduction of asymmetry and border cut‐off parameters according to the ABCD rule for dermoscopy , 2006, Journal of the European Academy of Dermatology and Venereology : JEADV.
[4] S. Menzies,et al. Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. , 1996, Archives of dermatology.
[5] Chu-Song Chen,et al. Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[7] Harald Kittler,et al. Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .
[8] Randy H. Moss,et al. A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..
[9] R. Hofmann-Wellenhof,et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. , 2019, The Lancet. Oncology.
[10] C. Grana,et al. Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks. , 2020, Clinical journal of the American Society of Nephrology : CJASN.
[11] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[12] Huiyu Zhou,et al. A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images , 2015 .
[13] R. Johr. Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist. , 2002, Clinics in dermatology.
[14] Georg Langs,et al. Causability and explainability of artificial intelligence in medicine , 2019, WIREs Data Mining Knowl. Discov..
[15] H. Kittler,et al. Diagnostic accuracy of dermoscopy. , 2002, The Lancet. Oncology.
[16] Yan Li,et al. Deep Semantics-Preserving Hashing Based Skin Lesion Image Retrieval , 2017, ISNN.
[17] H. Kittler,et al. Diagnostic accuracy of dermoscopy/dermatoscopy , 2004 .
[18] Yasuhiro Fujisawa,et al. The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers , 2019, Front. Med..
[19] C. Ahn,et al. Dermatoscopy for melanoma and pigmented lesions. , 2012, Dermatologic clinics.
[20] Hod Lipson,et al. Convergent Learning: Do different neural networks learn the same representations? , 2015, FE@NIPS.
[21] A. Jemal,et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.
[22] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[23] Rita Cucchiara,et al. Exploiting color and topological features for region segmentation with recursive fuzzy C-means , 2002 .
[24] Costantino Grana,et al. Skin Lesion Segmentation Ensemble with Diverse Training Strategies , 2019, CAIP.
[25] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[26] G. Wiselin Jiji,et al. Content-based image retrieval in dermatology using intelligent technique , 2015, IET Image Process..
[27] Noel C. F. Codella,et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[28] Daniela Massi,et al. Dermoscopy improves accuracy of primary care physicians to triage lesions suggestive of skin cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[29] Xiang Li,et al. A Query-by-Example Content-Based Image Retrieval System of Non-melanoma Skin Lesions , 2009, MCBR-CDS.
[30] Costantino Grana,et al. Improving Skin Lesion Segmentation with Generative Adversarial Networks , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).
[31] Stephen W Dusza,et al. The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. , 2007, Journal of the American Academy of Dermatology.
[32] Alfonso Baldi,et al. Definition of an automated Content-Based Image Retrieval (CBIR) system for the comparison of dermoscopic images of pigmented skin lesions , 2009, Biomedical engineering online.
[33] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Xavier Giro-i-Nieto,et al. Skin lesion classification from dermoscopic images using deep learning techniques , 2017, 2017 13th IASTED International Conference on Biomedical Engineering (BioMed).
[35] P. Aegerter,et al. Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. , 2001, Archives of dermatology.
[36] Samy Bengio,et al. Large Scale Online Learning of Image Similarity Through Ranking , 2009, J. Mach. Learn. Res..
[37] Costantino Grana,et al. Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification , 2021, 2020 25th International Conference on Pattern Recognition (ICPR).
[38] Khadidja Belattar,et al. Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis , 2017, J. Inf. Technol. Res..
[39] E. Gassner,et al. Automated melanoma recognition , 2001, IEEE Transactions on Medical Imaging.
[40] W. Stolz,et al. The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.
[41] Stephan Dreiseitl,et al. Do physicians value decision support? A look at the effect of decision support systems on physician opinion , 2005, Artif. Intell. Medicine.
[42] D. Rigel,et al. Malignant melanoma: Prevention, early detection, and treatment in the 21st century , 2000, CA: a cancer journal for clinicians.
[43] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[44] T. Kailath. The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .
[45] 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.
[46] Verónica Vilaplana,et al. BCN20000: Dermoscopic Lesions in the Wild , 2019, Scientific data.
[47] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[48] Philippe Schmid-Saugeona,et al. Towards a computer-aided diagnosis system for pigmented skin lesions. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[49] Prabir Bhattacharya,et al. Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images , 2016, 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).