Hybrid multiscale landmark and deformable image registration by Dana Paquin, Doron Levy, Lei Xing

“…Vese, A multiscale image representationusing hierarchical $(BV,L^2)$ decompositions, Multiscale Modeling andSimulations, vol. 2, no. 4, pp. 554--579, 2004, is reviewed, and animage registration algorithm is developed based on combining themultiscale decomposition with landmark and deformable techniques.Successful registration of medical images is achieved by firstobtaining a hierarchical multiscale decomposition of the images andthen using landmark-based registration to register the resultingcoarse scales. …”
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Effect of Different Parameter Values for Pre-processing of Using Mammography Images by Hanife Avcı, Jale Karakaya

“…Computer-assisted diagnosis (CAD) models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. …”
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Answer Distillation Network With Bi-Text-Image Attention for Medical Visual Question Answering by Hongfang Gong, Li Li

“…Medical Visual Question Answering (Med-VQA) is a multimodal task that aims to obtain the correct answers based on medical images and questions. Med-VQA, as a classification task, is typically more challenging for algorithms to predict answers to open-ended questions than to closed-ended questions due to the larger number of answer categories for the former. …”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

“…Recent research has focused on the application of artificial intelligence (AI) and radiomics in medical image processing, diagnosis, and treatment planning.MethodsA review of the current literature was conducted, analyzing the use of AI models and texture analysis for MS lesion segmentation and classification. …”
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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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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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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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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot be generalized well because of the lack of anomalous datasets. …”
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Deep Learning-Based Fully Automatic Segmentation of the Paranasal Sinuses in Chronic Rhinosinusitis Patients Using Computed Tomographic Images by Yuhang Wang, Xiaolei Zhang, Weidong Du, Na Dai, Yi Lyv, Keying Wu, Yiyang Tian, Yuxin Jie, Yu Lin, Weipiao Kang

“…We chose a custom 3D nnU-Net v2 network model because of its excellent performance in the field of 3D medical image segmentation, especially in automated training and accurate segmentation of complex structures. …”
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PLZero: placeholder based approach to generalized zero-shot learning for multi-label recognition in chest radiographs by Chengrong Yang, Qiwen Jin, Fei Du, Jing Guo, Yujue Zhou

“…Abstract By leveraging large-scale image-text paired data for pre-training, the model can efficiently learn the alignment between images and text, significantly advancing the development of zero-shot learning (ZSL) in the field of intelligent medical image analysis. However, the heterogeneity between cross-modalities, false negatives in image-text pairs, and domain shift phenomena pose challenges, making it difficult for existing methods to effectively learn the deep semantic relationships between images and text. …”
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Transfer learning for non-image data in clinical research: A scoping review. by Andreas Ebbehoj, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman

“…While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. …”
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