Isfahan Artificial Intelligence Event 2023: Lesion Segmentation and Localization in Magnetic Resonance Images of Patients with Multiple Sclerosis by Fariba Davanian, Iman Adibi, Mahnoosh Tajmirriahi, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Mohammad Reza Iravani, Kian Shahriari, Nesa Sharifi, Sadaf Moharreri, Farnaz Sedighin, Hossein Rabbani

“…Most of the teams preferred to use deep learning methods. The methods varied from a simple U-net structure to more complicated networks. …”
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Robust low frequency seismic bandwidth extension with a U-net and synthetic training data by P. Zwartjes, J. Yoo

“…Instead, our synthetic training data is created from individual randomly perturbed events with variations in bandwidth, making it more adaptable to different data sets compared to previous deep learning methods. The method was tested on both synthetic and real seismic data, demonstrating effective low frequency reconstruction and sidelobe reduction. …”
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Cross-Scale Guidance Integration Transformer for Instance Segmentation in Pathology Images by Yung-Ming Kuo, Jia-Chun Sheng, Chen-Hsuan Lo, You-Jie Wu, Chun-Rong Huang

“…<italic>Results:</italic> Compared with recent task-specific deep learning methods, our method can achieve state-of-the-art performance in two public gland cell datasets. …”
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Illuminant estimation method based on Color Lines and dichroic reflection model by Soshun Muto, Mashiho Mukaida, Noriaki Suetake

“…Various methods for illuminant estimation have been proposed, including hypothesis based approaches, deep learning methods, and methods based on the dichroic reflection model. …”
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Fault Recognition Method and Application Based on Generative Adversarial Network by Shuiliang Luo, Yongmei Huang, Yun Su, Shengkui Wang, Qianqian Liu, Yingqiang Qi, Fuhao Chang

“…The experimental results show that compared with the traditional deep learning method, this method shows significant advantages in fault recognition. …”
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Damage Identification of Conduit Rack in Offshore Platform Structures Based on a Novel Composite Neural Network by Jiaqiang Yan, Yuanchao Qiu, Renhe Shao, Ziqiao Ling, Ruixiang Zhang

“…Experimentally validated by finite element model simulation and testbed construction, our proposed NRBO-TCN-BiLSTM combined neural network damage identification accuracy is as high as 99 % on average, exceeding existing deep learning methods. The method has a wide range of applications in SHM for offshore platforms.…”
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Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes by Kader Azlağ Pekince, Adem Pekince, Buse Yaren Kazangirler

“…The aim of this study is to enable the detection and diagnosis of mandibular condyle degenerations, which are difficult to observe and diagnose on panoramic radiographs, using deep learning methods. <b>Methods</b>: A total of 3875 condylar images were obtained from panoramic radiographs. …”
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