Explainable AI-Based Approach for Age-Related Macular Degeneration (AMD) Detection via Fundus Imaging by Ainhoa Osa-Sanchez, Hossam Magdy Balaha, Ali Mahmoud, Ashraf Sewelam, Mohammed Ghazal, Begonya Garcia-Zapirain, Ayman El-Baz

“…Previous studies have demonstrated the efficacy of Vision Transformers (ViTs) in classifying medical images by successfully detecting retinal disorders such as AMD. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. …”
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TransDeep: Transformer-Integrated DeepLabV3+ for Image Semantic Segmentation by Tengfei Chai, Zhiguo Xiao, Xiangfeng Shen, Qian Liu, NianFeng Li, Tong Guan, Jia Tian

“…In recent years, image semantic segmentation algorithms have made significant progress driven by deep learning technology, and are widely used in fields such as medical image analysis, assistive technology for the visually impaired people, and autonomous driving. …”
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Fully automatic fossa ovalis segmentation from computed tomography images using deep neural network with atlas-based localization by Gakuto Aoyama, Toru Tanaka, Yukiteru Masuda, Naoki Matsuki, Ryo Ishikawa, Masahiko Asami, Kiyohide Satoh, Takuya Sakaguchi

“…At present, this information is obtained manually from pre-procedural medical images, which is time consuming with limited reproducibility. …”
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A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data by Vasileios Skaramagkas, Ioannis Kyprakis, Georgia S. Karanasiou, Dimitris I. Fotiadis, Manolis Tsiknakis

“…DL models can analyze vast and complex datasets, including patient-reported outcomes, medical images, and physiological signals, enabling a deeper understanding of factors influencing an individual's QoL. …”
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LMFUNet: A Lightweight Multi-fusion UNet Based on Spiking Neural Systems for Skin Lesion Segmentation by Ningkang Hu, Bing Li, Hong Peng, Zhicai Liu, Jun Wang

“…Skin lesion segmentation is critical in medical image processing, but the segmentation task faces numerous challenges due to the differences in size, color, shape, and texture of skin lesions between patients, as well as the blurring of the boundary between lesions and normal skin. …”
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Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images by Mazen Soufi, Yoshito Otake, Makoto Iwasa, Keisuke Uemura, Tomoki Hakotani, Masahiro Hashimoto, Yoshitake Yamada, Minoru Yamada, Yoichi Yokoyama, Masahiro Jinzaki, Suzushi Kusano, Masaki Takao, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato

“…Abstract Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. …”
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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…Future work will explore the potential applications of the developed models to other medical image analysis tasks.…”
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Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches by Seifeddine Messaoud, Soulef Bouaafia, Amna Maraoui, Lazhar Khriji, Ahmed Chiheb Ammari, Mohsen Machhout

“…However, technological tests based on deep learning techniques and medical images could be useful in fighting this pandemic. …”
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Partial Volume Reduction by Interpolation with Reverse Diffusion by Olivier Salvado, Claudia M. Hillenbrand, David L. Wilson

“…Many medical images suffer from the partial volume effect where a boundary between two structures of interest falls in the midst of a voxel giving a signal value that is a mixture of the two. …”
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Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…Brain tumor segmentation is one of the most challenging tasks of medical image analysis. The diagnosis of patients with gliomas is based on the analysis of magnetic resonance images and manual segmentation of tumor boundaries. …”
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Hepatic macrophages in liver fibrosis: pathogenesis and potential therapeutic targets by Hai Li, Hong You, Jidong Jia, Xu Fan

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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…This advancement is essential in addressing the critical need for clinical accuracy in medical image segmentation. NURBS enables the creation of continuous curves that seamlessly conform to the intricate contours of anatomical structures, offering a significant improvement in segmentation accuracy. …”
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Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data by H. T. Rüdisser, A. Windisch, U. V. Amerstorfer, C. Möstl, T. Amerstorfer, R. L. Bailey, M. A. Reiss

“…For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. …”
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Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images by Muqaddas Abid, Muhammad Suzuri Hitam, Rozniza Ali, Hamed Azami, Anne Humeau-Heurtier

“…Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. …”
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An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification by Aijaz Ahmad Reshi, Furqan Rustam, Arif Mehmood, Abdulaziz Alhossan, Ziyad Alrabiah, Ajaz Ahmad, Hessa Alsuwailem, Gyu Sang Choi

“…Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. …”
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Distributed Movement Control for Building a Ring in Mobile Wireless Sensor Networks by Jian Chen, Jie Jia, Yingyou Wen, Dazhe Zhao

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Edge-Preserving Probabilistic Downsampling for Reliable Medical Segmentation in Resource-Constrained Environments by Shahzad Ali, Yu Rim Lee, Soo Young Park, Won Young Tak, Soon Ki Jung

“…Preserving critical information during label downsampling is particularly crucial for medical image segmentation. This study introduces a novel approach, Edge-Preserving Probabilistic Downsampling (EPD), designed to retain critical details and bridge the performance gap between networks trained on original and downsampled resolutions. …”
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Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network by D. A. Gavrilov, E. I. Zakirov, E. V. Gameeva, V. Yu. Semenov, O. Yu. Aleksandrova

“…This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the fi eld of image processing and machine learning open up the prospect of crea ng systems based on ar fi cial neural convolu onal networks, superior to humans in problems of image classifi ca on, in par cular, in solving problems of analysis of various medical images. Among the most promising applica ons: automated recogni on and classifi ca on of skin diseases, detec on of pathologies on X-ray, CT, MRI, ultrasound imaging. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. It supports saving lives faster and rectifying some medical errors. …”
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