{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:32:27Z","timestamp":1792690347777,"version":"4.5.0"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.<\/jats:p>","DOI":"10.1038\/s41598-022-09929-9","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T11:29:47Z","timestamp":1649849387000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["A generative adversarial network for synthetization of regions of interest based on digital mammograms"],"prefix":"10.1038","volume":"12","author":[{"given":"Olaide N.","family":"Oyelade","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Absalom E.","family":"Ezugwu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubarak S.","family":"Almutairi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Apu Kumar","family":"Saha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haruna","family":"Chiroma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"issue":"4","key":"9929_CR1","doi-asserted-by":"publisher","first-page":"e6629","DOI":"10.1002\/cpe.6629","volume":"34","author":"O Oyelade","year":"2022","unstructured":"Oyelade, O. & Ezugwu, A. E. Characterization of abnormalities in breast cancer images using nature-inspired metaheuristic optimized convolutional neural networks model. Concurr. Comput. Pract. Exp. 34(4), e6629 (2022).","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"1","key":"9929_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-98978-7","volume":"11","author":"ON Oyelade","year":"2021","unstructured":"Oyelade, O. N. & Ezugwu, A. A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images. Sci. Rep. 11(1), 1\u201328 (2021).","journal-title":"Sci. Rep."},{"key":"9929_CR3","doi-asserted-by":"publisher","first-page":"77905","DOI":"10.1109\/ACCESS.2021.3083516","volume":"9","author":"ON Oyelade","year":"2021","unstructured":"Oyelade, O. N., Ezugwu, A. E. & Chiroma, H. CovFrameNet: An enhanced deep learning framework for COVID-19 detection. IEEE Access 9, 77905\u201377919. https:\/\/doi.org\/10.1109\/ACCESS.2021.3083516 (2021).","journal-title":"IEEE Access"},{"key":"9929_CR4","doi-asserted-by":"publisher","first-page":"102366","DOI":"10.1016\/j.bspc.2020.102366","volume":"65","author":"O Oyelade","year":"2020","unstructured":"Oyelade, O. & Ezugwu, A. A deep learning model using data augmentation of digital mammograms for detection of architectural distortion in whole images and patches. Biomed. Signal Process. Control 65, 102366 (2020).","journal-title":"Biomed. Signal Process. Control"},{"key":"9929_CR5","unstructured":"Dertat, A. Applied deep learning\u2014Part 4: convolutional neural networks. (2017) https:\/\/towardsdatascience.com\/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2 (Accessed 13 Mar 2020)."},{"key":"9929_CR6","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Yang, M., Tian, X., Jiang, N. & Wang, D. A full stage data augmentation method in deep convolutional neural network for natural image classification. Cogn. Model. Multimodal Data Intens. Syst. Appl. Nat. Soc. (COMDICS), 11 (2020).","DOI":"10.1155\/2020\/4706576"},{"key":"9929_CR7","unstructured":"O'Gara, S. & McGuinness, K. Comparing data augmentation strategies for deep image classification. In IMVIP 2019: Irish Machine Vision & Image Processing (Technological University Dublin, 2019)."},{"issue":"60","key":"9929_CR8","first-page":"1","volume":"6","author":"C Shorten","year":"2020","unstructured":"Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6(60), 1\u201348 (2020).","journal-title":"J. Big Data"},{"key":"9929_CR9","unstructured":"Goodfellow, J. I. et al. Generative adversarial network. NIPS (2014)."},{"key":"9929_CR10","unstructured":"Xin, Y., Ekta, W. & Paul, B. Generative Adversarial Network in Medical Imaging: A Review, 29. http:\/\/arxiv.org\/abs\/1809.07294 (2019)."},{"key":"9929_CR11","unstructured":"Sharma, P. What are Generative Models and GANs? The Magic of Computer Vision, (2020) https:\/\/medium.com\/analytics-vidhya\/what-are-generative-models-and-gans-the-magic-of-computer-vision-86b813c4fac9 (Accessed 12 Feb 2020)."},{"key":"9929_CR12","unstructured":"Changhee, H., Kohei, M., Shin'ichi, S. & Hideki, N. Learning more with less: GAN-based medical image augmentation. Med. Imaging Technol. 6 (2019)."},{"key":"9929_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Wang, H., Lu, H., Won, D. & Yoon, S. W. Medical image synthesis with generative adversarial networks for tissue recognition. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), https:\/\/doi.org\/10.1109\/ICHI.2018.00030 (2018).","DOI":"10.1109\/ICHI.2018.00030"},{"issue":"4","key":"9929_CR14","doi-asserted-by":"publisher","first-page":"159","DOI":"10.18383\/j.tom.2018.00042","volume":"4","author":"K Kazuhiro","year":"2018","unstructured":"Kazuhiro, K. et al. Generative adversarial networks for the creation of realistic artificial brain magnetic resonance images. Tomography. 4(4), 159\u2013163 (2018).","journal-title":"Tomography."},{"key":"9929_CR15","doi-asserted-by":"crossref","unstructured":"Wu, E., Wu, K., Cox, D. & Lotter, W. Conditional infilling GANs for data augmentation in mammogram classification. In MICCAI 2018, Breast Image Analysis Workshop, (2018).","DOI":"10.1007\/978-3-030-00946-5_11"},{"key":"9929_CR16","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"F-A Maayan","year":"2018","unstructured":"Maayan, F.-A. et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321\u2013331 (2018).","journal-title":"Neurocomputing"},{"key":"9929_CR17","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5967\u20135976 (2017).","DOI":"10.1109\/CVPR.2017.632"},{"key":"9929_CR18","unstructured":"Emami, H., Aliabadi, M. M., Dong, M. & Chinnam, R. B. SPA-GAN: Spatial Attention GAN forImage-to-Image Translation http:\/\/arxiv.org\/abs\/1908.06616 (2019)."},{"key":"9929_CR19","doi-asserted-by":"crossref","unstructured":"Liu, S. et al. Face Aging with Contextual Generative Adversarial Nets. In ACM-MM \u201917 (2017).","DOI":"10.1145\/3123266.3123431"},{"key":"9929_CR20","doi-asserted-by":"crossref","unstructured":"Antipov, G., Baccouche, M. & Dugelay, J. L. Face Aging with Conditional Generative Adversarial Networks (2017).","DOI":"10.1109\/ICIP.2017.8296650"},{"key":"9929_CR21","unstructured":"Pan, J. et al. SalGAN: visual saliency prediction with adversarial networks. (2017)."},{"key":"9929_CR22","doi-asserted-by":"crossref","unstructured":"Han Zhang, T. X. H. L., Zhang, S., Wang, X., Huang, X. & Metaxas, D. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (2017).","DOI":"10.1109\/ICCV.2017.629"},{"key":"9929_CR23","unstructured":"Quach, K. An AI system has just created the most realistic looking photos ever. https:\/\/www.theregister.co.uk\/2018\/12\/14\/ai_created_photos\/ (Accessed 16 Mar 2020)."},{"key":"9929_CR24","doi-asserted-by":"crossref","unstructured":"Ledig, C. et al. Photo-realistic single image super-resolution using a generative adversarial network. In CVRP (2017).","DOI":"10.1109\/CVPR.2017.19"},{"key":"9929_CR25","unstructured":"Surma, G. Image generator\u2014drawing cartoons with generative adversarial networks (2019) https:\/\/towardsdatascience.com\/image-generator-drawing-cartoons-with-generative-adversarial-networks-45e814ca9b6b (Accessed 16 Mar 2020)."},{"key":"9929_CR26","unstructured":"Sharma, N. My MangaGAN: building my first generative adversarial network (2019) https:\/\/heartbeat.fritz.ai\/my-mangagan-building-my-first-generative-adversarial-network-2ec1920257e3 (Accessed 16 Mar 2020)."},{"key":"9929_CR27","doi-asserted-by":"crossref","unstructured":"Tian, Y., Peng, X., Zhao, L., Zhang, S. & Metaxas, D. N. CR-GAN: learning complete representations for multi-view generation. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), 942\u2013948 (2018).","DOI":"10.24963\/ijcai.2018\/131"},{"key":"9929_CR28","doi-asserted-by":"crossref","unstructured":"Huang, R., Zhang, S., Li, T. & He, R. Beyond face rotation: global and local perception GAN for photorealistic and identity preserving frontal view synthesis, 1\u201311 (2017).","DOI":"10.1109\/ICCV.2017.267"},{"issue":"8","key":"9929_CR29","first-page":"1","volume":"14","author":"A Siarohin","year":"2015","unstructured":"Siarohin, A., Lathuili\u00e8re, S., Sangineto, E. & Sebe, N. Appearance and pose-conditioned human image generation using deformable GANs. J. Latex Class Files 14(8), 1\u201316 (2015).","journal-title":"J. Latex Class Files"},{"key":"9929_CR30","doi-asserted-by":"crossref","unstructured":"Siarohin, A., Sangineto, E., Lathuiliere, S. & Sebe, N. Deformable GANs for pose-based human image generation. In CVPR, 3408\u20133416 (2018).","DOI":"10.1109\/CVPR.2018.00359"},{"key":"9929_CR31","unstructured":"Hui, J. AN\u2014Some cool applications of GAN (2019) https:\/\/medium.com\/@jonathan_hui\/gan-some-cool-applications-of-gans-4c9ecca35900. (Accessed 16 Mar 2020)."},{"key":"9929_CR32","unstructured":"Radpour, N. D. & Bheda, V. Conditional generative adversarial networks for emoji synthesis with word embedding manipulation. http:\/\/arxiv.org\/abs\/1712.04421 (2018)."},{"key":"9929_CR33","unstructured":"Radhakrishnan, P. GAN for photo editing. (2017) https:\/\/buzzrobot.com\/photoshop-2-0-gan-for-photo-editing-3ba4eddceddd (Accessed 16 Mar 2020)."},{"key":"9929_CR34","doi-asserted-by":"crossref","unstructured":"Heinrich, G. Photo editing with generative adversarial networks. (2017) https:\/\/devblogs.nvidia.com\/photo-editing-generative-adversarial-networks-1\/ (Accessed 16 Mar 2020].","DOI":"10.1007\/978-981-33-6048-8_1"},{"key":"9929_CR35","unstructured":"Huikai, W., Shuai, Z., Junge, Z. & Kaiqi, H. GP-GAN: Towards Realistic High-Resolution Image Blending (2017)."},{"key":"9929_CR36","doi-asserted-by":"crossref","unstructured":"Zhan, F. & Zhu, H. Spatial fusion GAN for image synthesis. In CVPR (2019).","DOI":"10.1109\/CVPR.2019.00377"},{"key":"9929_CR37","unstructured":"Lv, B., Liu, Y., Zhang, S., Zeng, H. & Zhu, G. Super resolution with generative adversarial networks. In Association for the Advancement of Artificial (2018)."},{"key":"9929_CR38","unstructured":"Demir, U. & Unal, G. Patch-Based Image Inpainting with Generative Adversarial Networks http:\/\/arxiv.org\/abs\/1803.07422 (2018)."},{"key":"9929_CR39","doi-asserted-by":"crossref","unstructured":"Siavelis, P. R., Lamprinou, N. & Psarakis, E. Z. An improved GAN semantic image inpainting. In Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science, Vol. 12002 (2020).","DOI":"10.1007\/978-3-030-40605-9_38"},{"issue":"21","key":"9929_CR40","first-page":"148","volume":"382","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Sun, Y., Liu, L. & Xu, X. CascadeGAN: A category-supervised cascading generative adversarial network for clothes translation from the human body to tiled images. Neuro Comput. 382(21), 148\u2013161 (2020).","journal-title":"Neuro Comput."},{"key":"9929_CR41","unstructured":"Alphamoon. Using GANs to dress up your photo. (2018) https:\/\/blog.goodaudience.com\/using-gans-to-dress-up-your-photo-c300de7330ef (Accessed 16 Mar 2020)."},{"key":"9929_CR42","unstructured":"Abdullayeva, A. Clothes and color extraction with Generative Adversarial Network (2019) https:\/\/towardsdatascience.com\/clothes-and-color-extraction-with-generative-adversarial-network-80ba117e17e6 (Accessed 16 Mar 2020)."},{"key":"9929_CR43","unstructured":"Jang, Y., Kim, G. & Song, Y. Video prediction with appearance and motion conditions. In Proceedings of the 35th International Conference on Machine (2018)."},{"key":"9929_CR44","doi-asserted-by":"crossref","unstructured":"Kwon, Y.-H. & Park, M.-G. Predicting future frames using retrospective cycle GAN. In CVPR, 11811\u201311820 (2019).","DOI":"10.1109\/CVPR.2019.00191"},{"issue":"2","key":"9929_CR45","doi-asserted-by":"publisher","first-page":"697","DOI":"10.2991\/ijcis.d.190617.001","volume":"12","author":"H Li","year":"2019","unstructured":"Li, H., Zheng, Y., Wu, X. & Cai, Q. 3D model generation and reconstruction using conditional generative adversarial network. Int. J. Comput. Intell. Syst. 12(2), 697\u2013705 (2019).","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"9929_CR46","unstructured":"Fukamizu, K., Kondo, M. & Sakamoto, R. Generation High Resolution 3D Model from Natural Language by Generative Adversarial Network http:\/\/arxiv.org\/abs\/1901.07165 (2019)."},{"key":"9929_CR47","doi-asserted-by":"crossref","unstructured":"Zhu, J., Xie, J.-C. & Fang, Y. Learning adversarial 3 D model generation with 2 D image enhancer. In Conference Proceedings (2018).","DOI":"10.1609\/aaai.v32i1.12223"},{"issue":"2","key":"9929_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"54","author":"Z Wang","year":"2021","unstructured":"Wang, Z., She, Q. & Ward, T. E. Generative adversarial networks in computer vision: A survey and taxonomy. ACM Comput. Surv. (CSUR) 54(2), 1\u201338 (2021).","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"9929_CR49","doi-asserted-by":"publisher","first-page":"101552","DOI":"10.1016\/j.media.2019.101552","volume":"58","author":"X Yi","year":"2019","unstructured":"Yi, X., Walia, E. & Babyn, P. S. Generative adversarial network in medical imaging: A review. Med. Image Anal. 58, 101552 (2019).","journal-title":"Med. Image Anal."},{"key":"9929_CR50","unstructured":"Kazeminia, S. GANs for Medical Image Analysis http:\/\/arxiv.org\/abs\/1809.06222 (2019)."},{"key":"9929_CR51","unstructured":"Google, D. Common problems. (2020) https:\/\/developers.google.com\/machine-learning\/gan\/problems (Accessed 17 Mar 2020)."},{"key":"9929_CR52","unstructured":"Jonathan, H. GAN\u2014Why it is so hard to train Generative Adversarial Networks! (2018) https:\/\/medium.com\/@jonathan_hui\/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b (Accessed 17 Mar 2020)."},{"key":"9929_CR53","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.mcm.2009.05.014","volume":"50","author":"I Baeza","year":"2009","unstructured":"Baeza, I., Verdoy, J. A., Villanueva-Oller, J. & Villanueva, R. J. ROI-based procedures for progressive transmission of digital images: A comparision. Math. Comput. Model. 50, 849\u2013859 (2009).","journal-title":"Math. Comput. Model."},{"key":"9929_CR54","unstructured":"Chen, X. et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. http:\/\/arxiv.org\/abs\/1606.03657v1 [cs.LG] (2016)."},{"key":"9929_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, H. et al. StackGAN: Text to Photo-realistic Image Synthesiswith Stacked Generative Adversarial Networks. http:\/\/arxiv.org\/abs\/1612.03242v1 [cs.CV], (2016).","DOI":"10.1109\/ICCV.2017.629"},{"key":"9929_CR56","unstructured":"Wang, X. & Gupta, A. Generative Image Modeling using Style andStructure Adversarial Networks http:\/\/arxiv.org\/abs\/1603.05631v2 [cs.CV], (2016)."},{"key":"9929_CR57","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Bengio, Y., Dosovitskiy, A. & Clune, J. Plug & Play Generative Networks:Conditional Iterative Generation of Images in Latent Space\" http:\/\/arxiv.org\/abs\/1612.00005v1[cs.CV], (2016).","DOI":"10.1109\/CVPR.2017.374"},{"key":"9929_CR58","unstructured":"Neff, T. Data Augmentation in Deep Learning using Generative Adversarial Networks Thesis: Graz University of Technology, Graz, Austria (2018)."},{"key":"9929_CR59","unstructured":"Arjovsky, M., Chintala, S. & Bottou, L. Wasserstein GAN http:\/\/arxiv.org\/abs\/1701.07875[stat.ML], (2017)."},{"key":"9929_CR60","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. & Courville, A. Improved Training of Wasserstein GANs http:\/\/arxiv.org\/abs\/1704.00028v3 [cs.LG], (2017)."},{"key":"9929_CR61","doi-asserted-by":"crossref","unstructured":"Mao, X. et al. Least Squares Generative Adversarial Networks http:\/\/arxiv.org\/abs\/1611.04076 [cs.CV], (2017).","DOI":"10.1109\/ICCV.2017.304"},{"key":"9929_CR62","unstructured":"Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C.-J. & Sch\u00f6lkopf, B. AdaGAN: Boosting Generative Models http:\/\/arxiv.org\/abs\/1701.02386 [stat.ML], (2017)."},{"key":"9929_CR63","unstructured":"Odena, A., Olah, C. & Shlens, J. Conditional Image Synthesis with Auxiliary Classifier GANs http:\/\/arxiv.org\/abs\/1610.09585v4 [stat.ML], (2017)."},{"key":"9929_CR64","unstructured":"Pan, J. et al. SalGAN: visual saliency prediction with adversarial networks. In Computer Vision and Image Understanding, 1\u20139 (2018)."},{"key":"9929_CR65","unstructured":"Wang, X. et al. ESRGAN: enhanced super-resolution generative adversarial networks. In ECCV 2018 Workshop. Won Region 3 in the PIRM2018-SR Challenge, Won Region 3, (2018)."},{"key":"9929_CR66","unstructured":"Eric, W., Kevin, W., David, C. & William, L. Conditional Infilling GANs for Data Augmentation in Mammogram Classification http:\/\/arxiv.org\/abs\/1807.08093v2, 2018."},{"key":"9929_CR67","unstructured":"Guibas, J. T., Virdi, T. S. & Li, P. S. Synthetic Medical Images from Dual GenerativeAdversarial Networks, 9 http:\/\/arxiv.org\/abs\/1709.01872v3 [cs.CV] (2018)."},{"key":"9929_CR68","unstructured":"Brock, A., Donahue, J. & Simonyan, K. Large Scale GAN Training for High Fidelity Natural Image Synthesis http:\/\/arxiv.org\/abs\/1809.11096v2 [cs.LG], (2019)."},{"key":"9929_CR69","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhai, X., Ritter, M., Lucic, M. & Houlsby, N. Self-Supervised GANs via Auxiliary Rotation Loss http:\/\/arxiv.org\/abs\/1811.11212 [cs.LG], (2019).","DOI":"10.1109\/CVPR.2019.01243"},{"key":"9929_CR70","doi-asserted-by":"crossref","unstructured":"Shaham, T. R., Dekel, T. & Michaeli, T. SinGAN: Learning a Generative Model from a Single Natural Image http:\/\/arxiv.org\/abs\/1905.01164v2 [cs.CV], (2019).","DOI":"10.1109\/ICCV.2019.00467"},{"key":"9929_CR71","doi-asserted-by":"crossref","unstructured":"Wu, H., Zheng, S., Zhang, J. & Huang, K. GP-GAN: Towards Realistic High-Resolution Image Blending. In ACMMM 2019 (2019).","DOI":"10.1145\/3343031.3350944"},{"key":"9929_CR72","unstructured":"Wu, E., Wu, K. & Lotter, W. Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms. In Proceedings of Machine Learning Research (MIDL) 2020 Conference (2020)."},{"key":"9929_CR73","doi-asserted-by":"crossref","unstructured":"Oyelade, O. N. & Ezugwu, A. E. ArchGAN: a generative adversarial network for architectural distortion abnormalities in digital mammograms. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) (2021).","DOI":"10.1109\/ICECET52533.2021.9698751"},{"issue":"7","key":"9929_CR74","doi-asserted-by":"publisher","first-page":"3174","DOI":"10.1002\/int.22412","volume":"36","author":"Y Liu","year":"2021","unstructured":"Liu, Y. et al. An attention-based category-aware GRU model for the next POI recommendation. Int. J. Intell. Syst. 36(7), 3174\u20133189 (2021).","journal-title":"Int. J. Intell. Syst."},{"key":"9929_CR75","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1002\/int.22620","volume":"37","author":"Y Li","year":"2021","unstructured":"Li, Y. et al. A long short-term memory-based model for greenhouse climate prediction. Int. J. Intell. Syst. 37, 135\u2013151 (2021).","journal-title":"Int. J. Intell. Syst."},{"key":"9929_CR76","doi-asserted-by":"crossref","unstructured":"Liu, Y. Bidirectional GRU networks-based next POI category prediction for healthcare. Int. J. Intell. Syst. (2021).","DOI":"10.1002\/int.22710"},{"key":"9929_CR77","unstructured":"Korkinof, D., Rijken, T., O'Neill, M. & Yearsley, J. High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks http:\/\/arxiv.org\/abs\/1807.03401v1 (2018)."},{"key":"9929_CR78","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.3389\/fgene.2019.01243","volume":"10","author":"L Zhao","year":"2020","unstructured":"Zhao, L., Wang, J., Pang, L., Liu, Y. & Zhang, J. GANsDTA: predicting drug-target binding affinity using GANs. Front. Genet. 10, 1243. https:\/\/doi.org\/10.3389\/fgene.2019.01243 (2020).","journal-title":"Front. Genet."},{"key":"9929_CR79","doi-asserted-by":"publisher","first-page":"101684","DOI":"10.1016\/j.compmedimag.2019.101684","volume":"79","author":"K Armanious","year":"2020","unstructured":"Armanious, K. et al. MedGAN: Medical image translation using GANs. Comput. Med. Imaging Graph. 79, 101684 (2020).","journal-title":"Comput. Med. Imaging Graph."},{"key":"9929_CR80","doi-asserted-by":"crossref","unstructured":"Singh, N. K. & Raza, K. Medical image generation using generative adversarial networks: a review. In Health Informatics: A Computational Perspective in Healthcare (2021).","DOI":"10.1007\/978-981-15-9735-0_5"},{"key":"9929_CR81","unstructured":"Korkinof, D. et al. High-resolution mammogram synthesis using progressive generative adversarial networks. In Medical Imaging with Deep Learning (MIDL) Workshops (2019)."},{"key":"9929_CR82","unstructured":"Stewart, M. Simple Guide to Hyperparameter Tuning in Neural Networks (2019) https:\/\/towardsdatascience.com\/simple-guide-to-hyperparameter-tuning-in-neural-networks-3fe03dad8594 (Accessed 31 Mar 2020)."},{"key":"9929_CR83","unstructured":"AI, MC. How to fight mode collapse in GANs (2019) https:\/\/mc.ai\/how-to-fight-mode-collapse-in-gans\/ (Accessed 30 Mar 2020)."},{"key":"9929_CR84","unstructured":"Kashikar, P. Reducing Mode Collapse in GANs using Guided Latent Spaces (2019) https:\/\/medium.com\/intel-student-ambassadors\/reducing-mode-collapse-in-gans-using-guided-latent-spaces-36f52a08a668 (Accessed 30 Mar 2020)."},{"key":"9929_CR85","unstructured":"Ilya, T., Sylvain, G., Olivier, B., Carl-Johann, S.-G. & Bernhard, S. AdaGAN: Boosting Generative Models http:\/\/arxiv.org\/abs\/1701.02386 (2017)."},{"key":"9929_CR86","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kulharia, V., Namboodiri, V., Torr, P. H. S. & Dokania, P. K. Multi-Agent Diverse Generative Adversarial Networks http:\/\/arxiv.org\/abs\/1704.02906 (2018).","DOI":"10.1109\/CVPR.2018.00888"},{"key":"9929_CR87","unstructured":"Mescheder, L., Geiger, A. & Nowozin, S. Convergence and Stability of GAN Training https:\/\/avg.is.tuebingen.mpg.de\/projects\/convergence-and-stability-of-gan-training (Accessed 31 Mar 2020)."},{"key":"9929_CR88","unstructured":"Lars, M., Andreas, G. & Sebastian, N. Which Training Methods for GANs do actually Converge? http:\/\/arxiv.org\/abs\/1801.04406 (2018)."},{"key":"9929_CR89","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-Image Translation with Conditional Adversarial Networks http:\/\/arxiv.org\/abs\/1611.07004v1 (2016).","DOI":"10.1109\/CVPR.2017.632"},{"key":"9929_CR90","unstructured":"Salimans, T. et al. Improved Techniques for Training GANs http:\/\/arxiv.org\/abs\/1606.03498 (2016)."},{"key":"9929_CR91","unstructured":"Marchesi, M. Megapixel Size Image Creation using Generative Adversarial Networks http:\/\/arxiv.org\/abs\/1706.00082 (2017)."},{"key":"9929_CR92","doi-asserted-by":"crossref","unstructured":"Xi, P., Shu, C. & Goubran, R. Abnormality detection in mammography using deep convolutional neural networks. In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1\u20136 (2018).","DOI":"10.1109\/MeMeA.2018.8438639"},{"issue":"2","key":"9929_CR93","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira, I. C. et al. Inbreast: Toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236\u2013248. https:\/\/doi.org\/10.1016\/j.acra (2012).","journal-title":"Acad. Radiol."},{"key":"9929_CR94","doi-asserted-by":"crossref","unstructured":"Li, J. et al. Perceptual Generative Adversarial Networks for Small Object Detection http:\/\/arxiv.org\/abs\/1706.05274 (2017).","DOI":"10.1109\/CVPR.2017.211"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09929-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09929-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09929-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T02:03:06Z","timestamp":1669341786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09929-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,13]]},"references-count":94,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["9929"],"URL":"https:\/\/doi.org\/10.1038\/s41598-022-09929-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,13]]},"assertion":[{"value":"10 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6166"}}