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Jiang, J., Hu, Y.C., Tyagi, N., Zhang, P., Rimner, A., Mageras, G.S., Deasy, J.O. and Veeraraghavan, H. (2018) Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. and Fichtinger, G., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, Vol. 11071, Springer, Cham, 777-785.
https://doi.org/10.1007/978-3-030-00934-2_86

has been cited by the following article:

• TITLE: Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

  AUTHORS: Tudor Florin Ursuleanu, Andreea Roxana Luca, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Cristina Preda, Alexandru Grigorovici

  KEYWORDS: Medical Image Analysis, Data Types, Labels, Deep Learning Models

  JOURNAL NAME: E-Health Telecommunication Systems and Networks, Vol.10 No.2, June 24, 2021

  ABSTRACT: Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.