Transforming oral cancer care: The promise of deep learning in diagnosis by Durairaj Varalakshmi, Mayakrishnan Tharaheswari, Thirunavukarasou Anand, Konda Mani Saravanan

“…Deep learning (DL), a subset of artificial intelligence, holds promise for transforming medical image analysis and predictive analytics. In this perspective, we examine the applications of DL in oral cancer. …”
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Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration by Lotta M. Ellingsen, Jerry L. Prince

“…Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. …”
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3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images by Zhuliang Qian, Lifeng Xie, Yisheng Xu

“…Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. …”
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Cardiac and respiratory activities induce temporal changes in cerebral blood volume, balanced by a mirror CSF volume displacement in the spinal canal by Pan Liu, Kimi Owashi, Heimiri Monnier, Serge Metanbou, Cyrille Capel, Olivier Balédent

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Artificial intelligence for chest X-ray image enhancement by Liming Song, Hongfei Sun, Haonan Xiao, Sai Kit Lam, Yuefu Zhan, Ge Ren, Jing Cai

“…Recently, the successful application of deep learning (DL) algorithms in medical image analysis has prompted researchers to propose and design DL-based CXR image enhancement algorithms. …”
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Automated Techniques for the Interpretation of Fetal Abnormalities: A Review by Vidhi Rawat, Alok Jain, Vibhakar Shrimali

“…Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. …”
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EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS by S Sujatha, Pramod Pandey, D Gnana Rajesh

“…It is a specialized convolutional neural network that has shown significant potential in accurately segmenting intricate structures captured in medical images. It uses U-Net to precisely define the blood vessels from retinal images, facilitating accurate identification of macula regions for early AMD detection. …”
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Artificial intelligence in high-dose-rate brachytherapy treatment planning for cervical cancer: a review by Junyue Shi, Junyue Shi, Jun Chen, Gaokui He, Qinghe Peng

“…Recent advancements in artificial intelligence (AI), particularly in medical image processing, offer significant potential for automating and standardizing treatment planning in HDR-BT. …”
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Fast two dimensional to three dimensional registration of fluoroscopy and CT-scans using Octrees on segmentation maps by Luca Bertelli, Frédéric Gibou

“…We introduce a computationally efficient approach to the generation of Digital Reconstructed Radiographs (DRRs) needed to perform three dimensional to two dimensional medical image registration and apply this algorithm to virtual surgery. …”
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Radiomics in rectal cancer: current status of use and advances in research by Wei-Qin Huang, Ruo-Xuan Lin, Xiao-Hui Ke, Xiao-Hong Deng, Shi-Xiong Ni, Lina Tang

“…As an emerging technology, radiomics has gained widespread application in the diagnosis, assessment of treatment response, and analysis of prognosis for rectal cancer by extracting high-throughput quantitative features from medical images. Radiomics thus demonstrates considerable potential for optimizing clinical decision-making. …”
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Thickness Mapping of Eleven Retinal Layers Segmented Using the Diffusion Maps Method in Normal Eyes by Raheleh Kafieh, Hossein Rabbani, Fedra Hajizadeh, Michael D. Abramoff, Milan Sonka

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Transmantle pressure under the influence of free breathing: non-invasive quantification of the aqueduct pressure gradient in healthy adults by Pan Liu, Kimi Owashi, Heimiri Monnier, Serge Metanbou, Cyrille Capel, Olivier Balédent

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Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration by Yutong Liu, Balasrinivasa R. Sajja, Mariano G. Uberti, Howard E. Gendelman, Tammy Kielian, Michael D. Boska

“…To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). …”
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A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging by Mahir Kaya, Alper Özatılgan

“…Successful results in the detection of diseases from medical images with Convolutional Neural Networks (CNN) depend on the optimum creation of the number of layers and other hyper-parameters. …”
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A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach by Mohammed Benjelloun, Saïd Mahmoudi, Fabian Lecron

“…We propose a medical image segmentation approach based on the Active Shape Model theory. …”
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A QCT-Based Nonsegmentation Finite Element Head Model for Studying Traumatic Brain Injury by Zhaoyang Liang, Yunhua Luo

“…In the existing finite element head models (FEHMs) that are constructed from medical images, head tissues are usually segmented into a number of components according to the interior anatomical structure of the head. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…There is limited information about using Blind/No Reference (NR) quality evaluators in the medical image area. This paper shows the previously utilized Natural Image Quality Evaluator (NIQE) in CT scans; this NIQE is commonly used as a photolike image evaluator and provides an extensive assessment of the optimum NIQE setting. …”
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Alzheimer's disease image classification based on enhanced residual attention network. by Xiaoli Li, Bairui Gong, Xinfang Chen, Hui Li, Guoming Yuan

“…To address these issues, this study proposes a deep learning model to detect Alzheimer's disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. …”
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Exploring the Impact of Large Language Models on Disease Diagnosis by Ibrahim Almubark

“…This review shows that LLMs have utilized a variety of medical data sources, including general medical databases, specialized documents, medical images, and genomic data. Moreover, the focus of these LLMs spans a broad spectrum of healthcare fields, addressing chronic conditions, respiratory diseases, cancer, and rare diseases. …”
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AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

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