Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…Furthermore, transformer-based models have been utilized to tackle medical image processing problems by capitalizing on global connections among distant pixels in the images. …”
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3D Curvelet-Based Segmentation and Quantification of Drusen in Optical Coherence Tomography Images by M. Esmaeili, A. M. Dehnavi, H. Rabbani

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Observer- and sequence variability in personalized 4D flow MRI-based cardiovascular models by Belén Casas Garcia, Kajsa Tunedal, Federica Viola, Gunnar Cedersund, Carl-Johan Carlhäll, Matts Karlsson, Tino Ebbers

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Encryption of 3D or Higher-Dimensional Images: A Review by Renatta Nigro, Gabriel Ferreira, Maria Alice Andrade Calazans, Geovane Miguel, Felipe Alberto B. S. Ferreira, Verusca Severo, Francisco Madeiro

“…Precisely, the following classes of images are considered: light field images, point clouds, and 3D medical images. The security and performance aspects of the encryption schemes for images are analyzed. …”
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Analysis and Algorithmic Generation of Hepatic Vascular Systems by Lars Ole Schwen, Tobias Preusser

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MM-HiFuse: multi-modal multi-task hierarchical feature fusion for esophagus cancer staging and differentiation classification by Xiangzuo Huo, Shengwei Tian, Long Yu, Wendong Zhang, Aolun Li, Qimeng Yang, Jinmiao Song

“…Our findings provide evidence for the early diagnosis and accurate staging of esophageal cancer and serve as a new inspiration for the application of multi-modal multitask learning in medical image analysis. Code is available at https://github.com/huoxiangzuo/MM-HiFuse .…”
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OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation by Xueting Ren, Surong Chu, Guohua Ji, Zijuan Zhao, Juanjuan Zhao, Yan Qiang, Yangyang Wei, Yan Wang

“…However, they struggle with insufficient perception of small targets and gradient inconsistency in medical image detection tasks, hindering the full utilization of multi-scale features. …”
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Surface Approximation using Growing Self-Organizing Nets and Gradient Information by Jorge Rivera-Rovelo, Eduardo Bayro-Corrochano

“…Our algorithms were tested with several images, including medical images (CT and MR images). We include also some examples for the case of 3D surface estimation.…”
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Peering into the Heart: A Comprehensive Exploration of Semantic Segmentation and Explainable AI on the MnMs-2 Cardiac MRI Dataset by Ayoob Mohamed, Nettasinghe Oshan, Sylvester Vithushan, Bowala Helmini, Mohideen Hamdaan

“…Accurate and interpretable segmentation of medical images is crucial for computer-aided diagnosis and image-guided interventions. …”
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Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection by Amal Alshardan, Hany Mahgoub, Nuha Alruwais, Abdulbasit A. Darem, Wafa Sulaiman Almukadi, Abdullah Mohamed

“…Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. …”
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Information Security and Artificial Intelligence–Assisted Diagnosis in an Internet of Medical Thing System (IoMTS) by Pi-Yun Chen, Yu-Cheng Cheng, Zi-Heng Zhong, Feng-Zhou Zhang, Neng-Sheng Pai, Chien-Ming Li, Chia-Hung Lin

“…Hence, in this study, a symmetric encryption and decryption protocol was designed to ensure infosecurity of biosignals and medical images and assist in specific purposes in disease diagnosis. …”
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Application of Fractional Wave Packet Transform for Robust Watermarking of Mammograms by Pushpa Mala Siddaraju, Devappa Jayadevappa, Kaliyamoorthy Ezhilarasan

“…In this work, a robust nonblind medical image watermarking scheme is proposed. The process involves two steps: the embedding and the extraction phase. …”
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Application of Deep Dictionary Learning and Predefined Filters for Classification of Retinal Optical Coherence Tomography Images by Fariba Shaker, Zahra Baharlouei, Gerlind Plonka, Hossein Rabbani

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Computer Based Correlation of the Texture of P63 Expressed Nuclei with Histological Tumour Grade, in Laryngeal Carcinomas by Konstantinos Ninos, Spiros Kostopoulos, Ioannis Kalatzis, Panagiota Ravazoula, George Sakelaropoulos, George Panayiotakis, George Economou, Dionisis Cavouras

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A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images by Lih-Jen Kau, Tien-Lin Lee

“…Extensive experiments on natural images as well as medical images with subjective and objective evaluations will be given to demonstrate the usefulness of the proposed approach.…”
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Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images by Macarena Boix, Begoña Cantó

“…In this work we present an efficient segmentationmethod for medical image analysis. In particular, with this methodblood cells can be segmented. …”
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Quantifying Optical Microangiography Images Obtained from a Spectral Domain Optical Coherence Tomography System by Roberto Reif, Jia Qin, Lin An, Zhongwei Zhi, Suzan Dziennis, Ruikang Wang

“…Therefore, a quantitative method for characterizing the angiography obtained from medical images would have several clinical applications. …”
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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

“…Abstract Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. …”
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MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images by Tianhu Zhao, Tianhu Zhao, Yong Yue, Hang Sun, Jingxu Li, Yanhua Wen, Yudong Yao, Wei Qian, Yubao Guan, Shouliang Qi, Shouliang Qi

“…The model performs best on medical images with a 50% mask ratio, showing a 1.4% increase in accuracy compared to the optimal 75% mask ratio on natural images.DiscussionThe proposed MAEMC-NET effectively distinguishes between benign and malignant solitary pulmonary nodules and holds significant potential to assist radiologists in improving the diagnostic accuracy of PGN and SLA.…”
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An Optical Model for Translucent Volume Rendering and Its Implementation Using the Preintegrated Shear-Warp Algorithm by Bin Li, Lianfang Tian, Shanxing Ou

“…In order to efficiently and effectively reconstruct 3D medical images and clearly display the detailed information of inner structures and the inner hidden interfaces between different media, an Improved Volume Rendering Optical Model (IVROM) for medical translucent volume rendering and its implementation using the preintegrated Shear-Warp Volume Rendering algorithm are proposed in this paper, which can be readily applied on a commodity PC. …”
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