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Melanoma is caused by the abnormal growth of melanocytes in human skin.
“Assessing diagnostic skill in dermatology: a comparison between general practitioners and dermatologists,”
Hue Tran, Keng Chen, Adrian C Lim, James Jabbour, and Stephen Shumack, · 2005
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“Ph 2-a dermoscopic image database for research and benchmarking,”
Teresa Mendonça, Pedro M Ferreira, Jorge S Marques, André RS Marcal, and Jorge Rozeira, · 2013
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“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
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“Unet++: A nested u-net architecture for medical image segmentation,”
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang, · 2018
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“H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,”
Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, and Pheng-Ann Heng, · 2018
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“Attention u-net: Learning where to look for the pancreas,”
Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al., · 2018
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“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),”
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al., · 2018
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“Bi-directional convlstm u-net with densely connected convolutions,”
Reza Azad, Maryam Asadi-Aghbolaghi, Mahmood Fathy, and Sergio Escalera, · 2019
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Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al., · 2019
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“U2-net: Going deeper with nested u-structure for salient object detection,”
Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R Zaiane, and Martin Jagersand, · 2020
Cited alongside, same era.
“Semi-supervised few-shot learning for medical image segmentation,”
Abdur R Feyjie, Reza Azad, Marco Pedersoli, Claude Kauffman, Ismail Ben Ayed, and Jose Dolz, · 2020
Cited alongside, same era.
“An image is worth 16x16 words: Transformers for image recognition at scale,”
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al., · 2020
Cited alongside, same era.
“Multi-level context gating of embedded collective knowledge for medical image segmentation,”
Maryam Asadi-Aghbolaghi, Reza Azad, Mahmood Fathy, and Sergio Escalera, · 2020
Cited alongside, same era.
“Crossvit: Cross-attention multi-scale vision transformer for image classification,”
Chun-Fu Richard Chen, Quanfu Fan, and Rameswar Panda, · 2021
Later among the works it cites.
“Medical transformer: Gated axial-attention for medical image segmentation,”
Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel, · 2021
Later among the works it cites.
“Cancer statistics, 2022,”
Rebecca L Siegel, Kimberly D Miller, and Ahmedin Jemal, · 2022
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Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, and Dorit Merhof, · 2022
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“Transdeeplab: Convolution-free transformer-based deeplab v3+ for medical image segmentation,”
Reza Azad, Moein Heidari, Moein Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, Ehsan Adeli, and Dorit Merhof, · 2022
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Reza Azad, Abdur R Fayjie, Claude Kauffmann, Ismail Ben Ayed, Marco Pedersoli, and Jose Dolz, · 2021
Cited alongside, same era.
“Deep frequency re-calibration u-net for medical image segmentation,”
Reza Azad, Afshin Bozorgpour, Maryam Asadi-Aghbolaghi, Dorit Merhof, and Sergio Escalera, · 2021
Cited alongside, same era.
“Transunet: Transformers make strong encoders for medical image segmentation,”
Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou, · 2021
Cited alongside, same era.
“Swin-unet: Unet-like pure transformer for medical image segmentation,”
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang, · 2021
Cited alongside, same era.
“Swin transformer: Hierarchical vision transformer using shifted windows,”
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo, · 2021
Cited alongside, same era.
“Transnorm: Transformer provides a strong spatial normalization mechanism for a deep segmentation model,”
Reza Azad, Mohammad T AL-Antary, Moein Heidari, and Dorit Merhof, · 2022
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“Contextual attention network: Transformer meets u-net,”
Azad Reza, Heidari Moein, Wu Yuli, and Merhof Dorit, · 2022
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“Fat-net: Feature adaptive transformers for automated skin lesion segmentation,”
Huisi Wu, Shihuai Chen, Guilian Chen, Wei Wang, Baiying Lei, and Zhenkun Wen, · 2022
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