Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Untitled work

(2014) · Cited by 33,419 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

Cancer statistics, 2016

Siegel, R. L., Miller, K. D., & Jemal, A. (2016) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21332 · Cited by 16,003 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

Untitled work

(2010) · Cited by 14,134 · see citing works

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works

Final Version of 2009 AJCC Melanoma Staging and Classification

Balch, C. M., Gershenwald, J. E., Soong, S.-J., Thompson, J. F., Atkins, M. B., Byrd, D. R., Buzaid, A. C., Cochran, A. J., Coit, D. G., Ding, S., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2009.23.4799 · Cited by 4,592 · see citing works

THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE. ¹

Jaccard, P. (1912) · New Phytologist · doi:10.1111/j.1469-8137.1912.tb05611.x · Cited by 4,561 · see citing works

Learning Deconvolution Network for Semantic Segmentation

Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · see citing works

Untitled work

(2013) · Cited by 3,740 · see citing works

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735 · see citing works

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)

Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620 · see citing works

Stochastic Gradient Descent Tricks

Bottou, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_25 · Cited by 2,097 · see citing works

Static Analysis of Shape in TensorFlow ProgramsPreprint

The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629 · see citing works

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536 · see citing works

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P.-A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2642839 · Cited by 1,140 · see citing works

PH<sup>2</sup> - A dermoscopic image database for research and benchmarking

Mendonca, T., Ferreira, P. M., Marques, J. S., Marcal, A. R. S., & Rozeira, J. (2013) · 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610779 · Cited by 1,009 · see citing works

Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting

Vestergaard, M., Macaskill, P., Holt, P. E., & Menzies, S. W. (2008) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2008.08713.x · Cited by 896 · see citing works

A methodological approach to the classification of dermoscopy images

Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., & Moss, R. H. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.01.003 · Cited by 697 · see citing works

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679 · see citing works

Deep learning ensembles for melanoma recognition in dermoscopy images

Codella, N., Nguyen, Q.-B., Pankanti, S., Gutman, D. A., Helba, B., Halpern, A. C., & Smith, J. R. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2708299 · Cited by 665 · see citing works

Automated melanoma recognition

Ganster, H., Pinz, P., Röhrer, R., Wildling, E., Binder, M., & Kittler, H. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.918473 · Cited by 633 · see citing works

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Avendi, M. R., Kheradvar, A., & Jafarkhani, H. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.01.005 · Cited by 591 · see citing works

A CNN Regression Approach for Real-Time 2D/3D Registration

Miao, S., Wang, Z. J., & Liao, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2521800 · Cited by 514 · see citing works

Lesion border detection in dermoscopy images

Celebi, M. E., Iyatomi, H., Schaefer, G., & Stoecker, W. V. (2009) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2008.11.002 · Cited by 423 · see citing works

Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

Silveira, M., Nascimento, J. C., Marques, J. S., Marcal, A. R. S., Mendonça, T., Yamauchi, S., Maeda, J., & Rozeira, J. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2008.2011119 · Cited by 409 · see citing works

Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)Preprint

Gutman, D. A., Codella, N., Celebi, E., Helba, B., Marchetti, M. A., Mishra, N. K., & Halpern, A. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01397 · Cited by 365 · see citing works

Computerized analysis of pigmented skin lesions: A review

Korotkov, K., & García, R. (2012) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2012.08.002 · Cited by 355 · see citing works

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.

Cha, K., Hadjiiski, L., Samala, R. K., Chan, H., Caoili, E., & Cohan, R. (2016) · Medical Physics (Lancaster) · doi:10.1118/1.4944498 · Cited by 248 · see citing works

Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition

Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D., & Zhou, X. S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2524985 · Cited by 223 · see citing works

Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods

Celebi, M. E., Wen, Q., Hwang, S., et al. (2012) · Skin Research and Technology · doi:10.1111/j.1600-0846.2012.00636.x · Cited by 207 · see citing works

A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model

Ma, Z., & Tavares, J. M. R. S. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2390032 · Cited by 189 · see citing works

Combinations of Radiation Therapy and Immunotherapy for Melanoma: A Review of Clinical Outcomes

Barker, C. A., & Postow, M. A. (2014) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2013.08.035 · Cited by 161 · see citing works

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

Ahn, E., Kim, J., Bi, L., Kumar, A., Li, C., FULHAM, M. J., & Feng, D. D. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2653179 · Cited by 160 · see citing works

Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm

Xie, F., & Bovik, A. C. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.08.012 · Cited by 126 · see citing works

Mean shift based gradient vector flow for image segmentation

Zhou, H., Li, X., Schaefer, G., Celebi, M. E., & Miller, P. C. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.11.015 · Cited by 119 · see citing works

Segmentation of Dermoscopy Images Using Wavelet Networks

Sadri, A. R., Zekri, M., Sadri, S., Gheissari, N., Mokhtari, M., & Kolahdouzan, F. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2227478 · Cited by 87 · see citing works

Simpler, Faster, More Accurate Melanocytic Lesion Segmentation Through MEDS

Peruch, F., Bogo, F., Bonazza, M., Cappelleri, V.-M., & Peserico, E. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2283803 · Cited by 83 · see citing works

Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata

Bi, L., Kim, J., Ahn, E., Feng, D. D., & FULHAM, M. J. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493448 · Cited by 83 · see citing works

A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images

Celebi, M. E., Mendonca, T., & Marques, J. S. (2015) · CRC Press eBooks · doi:10.1201/b19107-8 · Cited by 76 · see citing works

Automated saliency-based lesion segmentation in dermoscopic images

Ahn, E., Bi, L., Jung, Y. H., Kim, J., Li, C., FULHAM, M. J., & Feng, D. D. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319025 · Cited by 62 · see citing works

Extraction of Skin Lesions from Non Dermoscopic Images Using Deep Learning

Begum, S., & Asra, S. (2017) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit1724124 · Cited by 16 · see citing works

Image segmentation using deep learning: A survey

Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796 · see citing works

Survey on deep learning for radiotherapy

Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works

Federated learning improves site performance in multicenter deep learning without data sharing

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · see citing works

Skin disease diagnosis with deep learning: a review

Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178 · see citing works

Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

Zhou, J., He, X., Sun, L., Xu, J., Chen, X., Chu, Y., Zhou, L., Liao, X., Zhang, B., Afvari, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50043-3 · Cited by 189 · see citing works

Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices

Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., Sayres, R., Kanada, K., Eng, C., Nagpal, K., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.7249 · Cited by 164 · see citing works

Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour

Albahli, S., Nida, N., Irtaza, A., Yousaf, M. H., & Mahmood, M. T. (2020) · IEEE Access · doi:10.1109/access.2020.3035345 · Cited by 126 · see citing works

<i>auto</i>SMIM: Automatic Superpixel-Based Masked Image Modeling for Skin Lesion Segmentation

Wang, Z., Lyu, J., & Tang, X. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3290700 · Cited by 35 · see citing works

Revamping AI models in dermatology: overcoming critical challenges for enhanced skin lesion diagnosis

Mehta, D., Betz-Stablein, B., Nguyen, T. D., Gal, Y., Bowling, A., Haskett, M., Sashindranath, M., Bonnington, P., Mar, V., Soyer, H. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.01009 · Cited by 3 · see citing works

Jaccard Distance (Jaccard Index, Jaccard Similarity Coefficient)

Hancock, J. M. (2004) · Dictionary of Bioinformatics and Computational Biology · doi:10.1002/9780471650126.dob0956 · Cited by 53 · see citing works

Utilization of Jaccard Index Measures on Multiple Attribute Group Decision Making under Neutrosophic Environment

Gayathri, N., Helen, D. M., & Mounika, P. (2020) · International Journal of Neutrosophic Science · doi:10.54216/ijns.030203 · Cited by 3 · see citing works

Improving Jaccard Index Using Genetic Algorithms for Collaborative Filtering

Lee, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61824-1_41 · Cited by 5 · see citing works

Improving Jaccard Index for Measuring Similarity in Collaborative Filtering

Lee, S. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-4154-9_93 · Cited by 27 · see citing works

Comparison of Similarity Coefficients on Morphological Rodent Tuber

Binanto, I., Warnars, H. L. H. S., Abbas, B. S., et al. (2018) · doi:10.1109/inapr.2018.8627050 · Cited by 9 · see citing works