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Jie Zhang, Fan Li, Xin Zhang, Yue Cheng and Xinhong Hei
As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduc...
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Yiheng Zhou, Kainan Ma, Qian Sun, Zhaoyuxuan Wang and Ming Liu
Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the sma...
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Shoffan Saifullah and Rafal Drezewski
Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preproc...
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Woonghee Lee and Younghoon Kim
This study introduces a deep-learning-based framework for detecting adversarial attacks in CT image segmentation within medical imaging. The proposed methodology includes analyzing features from various layers, particularly focusing on the first layer, a...
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Chuanbo Wang, Amirreza Mahbod, Isabella Ellinger, Adrian Galdran, Sandeep Gopalakrishnan, Jeffrey Niezgoda and Zeyun Yu
Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the ...
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Navid Khalili Dizaji and Mustafa Dogan
Brain tumors are one of the deadliest types of cancer. Rapid and accurate identification of brain tumors, followed by appropriate surgical intervention or chemotherapy, increases the probability of survival. Accurate determination of brain tumors in MRI ...
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Shubin Wang, Yuanyuan Chen and Zhang Yi
The structure and function of retinal vessels play a crucial role in diagnosing and treating various ocular and systemic diseases. Therefore, the accurate segmentation of retinal vessels is of paramount importance to assist a clinical diagnosis. U-Net ha...
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Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner...
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Eduardo Morales-Vargas, Hayde Peregrina-Barreto, Rita Q. Fuentes-Aguilar, Juan Pablo Padilla-Martinez, Wendy Argelia Garcia-Suastegui and Julio C. Ramirez-San-Juan
Microvasculature analysis is an important task in the medical field due to its various applications. It has been used for the diagnosis and threat of diseases in fields such as ophthalmology, dermatology, and neurology by measuring relative blood flow or...
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Junkang Qin, Xiao Wang, Dechang Mi, Qinmu Wu, Zhiqin He and Yu Tang
The study of human torso medical image segmentation is significant for computer-aided diagnosis of human examination, disease tracking, and disease prevention and treatment. In this paper, two application tasks are designed for torso medical images: the ...
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