Robust Multi-Subtype Identification of Breast Cancer Pathological Images Based on a Dual-Branch Frequency Domain Fusion Network by Jianjun Li, Kaiyue Wang, Xiaozhe Jiang

“…However, the extraction of key information from complex medical images and the attainment of high-precision classification present a significant challenge. …”
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ChatGPT-4.0 in oral and maxillofacial radiology: prediction of anatomical and pathological conditions from radiographic images by Shila Kahalian, Marieh Rajabzadeh, Melisa Öçbe, Mahmut Sabri Medisoglu

“…Introduction: ChatGPT has the ability to generate human-like text, analyze and understand medical images using natural Language processing (NLP) algorithms. …”
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Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs by Hassen Louati, Ali Louati, Khalid Mansour, Elham Kariri

“…This framework provides a practical, scalable solution to improve both the accuracy and efficiency of medical image classification.…”
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Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. by Solange Amorim Nogueira, Fernanda Ambrogi B Luz, Thiago Fellipe O Camargo, Julio Cesar S Oliveira, Guilherme Carvalho Campos Neto, Felipe Brazao F Carvalhaes, Marcio Rodrigues C Reis, Paulo Victor Santos, Giovanna Souza Mendes, Rafael Maffei Loureiro, Daniel Tornieri, Viviane M Gomes Pacheco, Antonio Paulo Coimbra, Wesley Pacheco Calixto

“…The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction.…”
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Transformer enabled multi-modal medical diagnosis for tuberculosis classification by Sachin Kumar, Shivani Sharma, Kassahun Tadesse Megra

“…This study presents a cross modal transformer-based fusion approach for multimodal clinical data analysis using medical images and clinical data. The proposed approach leverages the image embedding layer to convert image into visual tokens, and another clinical embedding layer to convert clinical data into text tokens. …”
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Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks by A.V. Dobshik, S.K. Verbitskiy, I.A. Pestunov, K.M. Sherman, Yu.N. Sinyavskiy, A.A. Tulupov, V.B. Berikov

“…Moreover, a special patches sampling strategy was used to address the large size of medical images and class imbalance and to stabilize neural network training. …”
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Integer wavelet transform‐based secret image sharing using rook polynomial and hamming code with authentication by Sara Charoghchi, Zahra Saeidi, Samaneh Mashhadi

“…In most of these schemes, the cover image cannot be recovered without distortion, which makes them useless in case of utilising critical cover images such as military or medical images. Also, embedding the secret data in Least significant bits of the cover image, in many of these schemes, makes them very fragile to steganlysis. …”
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Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology by Xinghuo Ye, Qianyi Wang

“…Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. …”
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Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review by M. A. D. Buser, J. K. van derRest, M. H. W. A. Wijnen, R. R. deKrijger, A. F. W. van derSteeg, M. M. van denHeuvel‐Eibrink, M. Reismann, S. Veldhoen, L. Pio, M. Markel

“…ABSTRACT Background Medical images play an important role in diagnosis and treatment of pediatric solid tumors. …”
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RadiomixNet: Integrating Radiomics and Feature Extraction for Advanced Pneumonia Diagnosis by Rahul Gowtham Poola, Siva Sankar Yellampalli

“…The research outlines a workflow for medical image analysis and disease diagnosis through radiomics feature extraction. …”
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GLClick: Interactive Segmentation Combining Global and Local Features by Jiaying Tang, Hongyuan Wang, Zongyuan Ding, Zihao Xin

“…Moreover, we conduct experiments on medical image datasets, further illustrating the model’s versatility and effectiveness across different domains.…”
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Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification by Xinyi Fang, Chak Fong Chong, Kei Long Wong, Marco Simões, Benjamin K. Ng

“…Abstract Data scarcity in medical images makes transfer learning a common approach in computer-aided diagnosis. …”
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A weak edge estimation based multi-task neural network for OCT segmentation. by Fan Yang, Pu Chen, Shiqi Lin, Tianming Zhan, Xunning Hong, Yunjie Chen

“…Secondly, the high cost of annotating medical image data results in a lack of labeled data, leading to overfitting during model training. …”
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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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Class Activation Map Guided Backpropagation for Discriminative Explanations by Yongjie Liu, Wei Guo, Xudong Lu, Lanju Kong, Zhongmin Yan

“…The proposed method has broad applicability in scenarios like model debugging, where it identifies causes of misclassification, and medical image diagnosis, where it enhances user trust by aligning visual explanations with clinical insights.…”
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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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Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise. by Shamik Tiwari, Akhilesh Kumar Sharma, Izzatdin Abdul Aziz, Deepak Gupta, Antima Jain, Hairulnizam Mahdin, Senthil Athithan, Rahmat Hidayat

“…Applications for Segmentation-Based Fractal Texture Features (SFTF) include image classification, texture generation, and medical image analysis. They are beneficial for examining textures with intricate, erratic patterns that are difficult to characterize using conventional statistical techniques accurately. …”
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2D MoS2-based reconfigurable analog hardware by Xinyu Huang, Lei Tong, Langlang Xu, Wenhao Shi, Zhuiri Peng, Zheng Li, Xiangxiang Yu, Wei Li, Yilun Wang, Xinliang Zhang, Xuan Gong, Jianbin Xu, Xiaoming Qiu, Hongyang Wen, Jing Wang, Xuebin Hu, Caihua Xiong, Yu Ye, Xiangshui Miao, Lei Ye

“…By assembling the functions to fit with different environment-interactive demanding tasks, this hardware experimentally achieves the reconstruction and image sharpening of medical images for diagnosis as well as circuit-level imitation of attention-switching and visual residual mechanisms for smart perception. …”
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