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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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Darian M. Onchis, Flavia Costi, Codruta Istin, Ciprian Cosmin Secasan and Gabriel V. Cozma
(1) Background: Lung cancers are the most common cancers worldwide, and prostate cancers are among the second in terms of the frequency of cancers diagnosed in men. Automatic ranking of the risk groups of such diseases is highly in demand, but the clinic...
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Yue Zha, Yuanzhi Ke, Xiao Hu and Caiquan Xiong
Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field. In this work, we propose a novel attention layer, called the ?ontology atte...
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Antonello Pasini and Stefano Amendola
Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of cer...
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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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Ioana-Raluca Zaman and Stefan Trausan-Matu
Neuropsychiatric disorders affect the lives of individuals from cognitive, emotional, and behavioral aspects, impact the quality of their lives, and even lead to death. Outside the medical area, these diseases have also started to be the subject of inves...
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Weiming Fan, Jiahui Yu and Zhaojie Ju
Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical structures, conventionally necessitates the arduous manual scrutiny of seasoned medical experts. Nevertheless, the recent strides in deep learning technologies proffer...
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Pedro Celard, Adrián Seara Vieira, José Manuel Sorribes-Fdez, Eva Lorenzo Iglesias and Lourdes Borrajo
In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Tem...
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Abdul Rahaman Wahab Sait and Ali Mohammad Alorsan Bani Awad
Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and comp...
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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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