Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia by Bo-Han Wei, Xavier Cheng-Hong Tsai, Kuo-Jui Sun, Min-Yen Lo, Sheng-Yu Hung, Wen-Chien Chou, Hwei-Fang Tien, Hsin-An Hou, Chien-Yu Chen

“…Abstract The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). …”
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Observational Diagnostics: The Building Block of AI-Powered Visual Aid for Dental Practitioners by Ruchika Raj, Ravikumar Rajappa, Vijayalakshmi Murthy, Mahyar Osanlouy, Daniel Lawrence, Mahen Ganhewa, Nicola Cirillo

“…Artificial intelligence (AI) has gained significant traction in medical image analysis, including dentistry, aiding clinicians in making timely and accurate diagnoses. …”
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Heavy and Lightweight Deep Learning Models for Semantic Segmentation: A Survey by Cristina Carunta, Alina Carunta, Calin-Adrian Popa

“…Semantic segmentation is an important computer vision task due to its numerous real-world applications such as autonomous driving, video surveillance, medical image analysis, robotics, augmented reality, among others, and its popularity increased with the development of deep learning approaches. …”
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Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features by Eman Magdy, Nourhan Zayed, Mahmoud Fakhr

“…Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. …”
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The role of radiomics in dentistry and oral radiology by Tannishtha ., Shruthi Hegde, G. Subhas Babu, Vidya Ajila, B.S. Shama

“…This new technology can quantify textural information through mathematical analysis from the region of interest in medical images, which the human eye cannot perceive. In oral and maxillofacial imaging, the use of cone beam computed tomography (CBCT) has been increasing that in turn encourages AI and radiomics research to assist clinicians in early diagnosis and effective treatment planning. …”
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Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network by Varun Magotra, Mukesh Kumar Rohil

“…The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. …”
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An intelligent system for lung CT image denoising using a hybrid WT-NLM filter by S.L. Soniya, T. Ajith Bosco Raj

“…The elimination of noise from the original image is a significant challenge for scientists and this work considers Gaussian, Salt and pepper noises, as medical images are prone to it. Hence, this work presents ways for mitigating noise, while preserving the relevant image information. …”
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Brain CT image classification based on mask RCNN and attention mechanism by Shoulin Yin, Hang Li, Lin Teng, Asif Ali Laghari, Ahmad Almadhor, Michal Gregus, Gabriel Avelino Sampedro

“…The application of machine learning, and block-chain techniques into medical image retrieval, classification and auxiliary diagnosis has become one of the research hotspots at present. …”
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A deep learning ICDNET architecture for efficient classification of histopathological cancer cells using Gaussian noise images by Hui Zong, Wenlong An, Xin Chen, Zhanhui Yang, Heng Zhou, Xiangchao Liu, Jianchu Lin, Chuanyue Zong

“…To address this issue, this study introduces a new hybrid network model, termed ICDNET, designed to fuse global and local features without destroying the integrity of the feature data, thus enhancing the accuracy of medical image classification. The ICDNET model consists of two main features: (i) a serial hierarchical structure composed of global and local feature blocks; and (ii) an Internal Communication Hierarchical Fusion Block (ICHF) and an Efficient Dual Self-Attention (EDA) mechanism. …”
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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…This study proposes a novel Content-Based Medical Image Retrieval (CBMIR) framework using Convolutional Neural Networks (CNN) and Transfer Learning (TL) for MS diagnosis using MRI data. …”
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Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. by Honghua Liu, Mingwei Zhao, Chang She, Han Peng, Mailan Liu, Bo Li

“…Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. …”
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Application and Thinking of Deep Learning in Predicting Lateral Cervical Lymph Node Metastasis of Papillary Thyroid Cancer by Shengli SHAO, Jiheng WANG, Shanting LIU

“…Deep learning is a primary method for medical image recognition or feature extraction. In recent years, deep learning-based ultrasound, CT, cytology, conventional clinical parameters, or multimodal models combining these data have been developed and are expected to achieve routine clinical application. …”
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A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer by Yin Li, Yin Li, Yin Li, Shuang Li, Shuang Li, Ruolin Xiao, Ruolin Xiao, Xi Li, Yongju Yi, Yongju Yi, Liangyou Zhang, Liangyou Zhang, You Zhou, You Zhou, Yun Wan, Chenhua Wei, Liming Zhong, Liming Zhong, Wei Yang, Wei Yang, Lin Yao, Lin Yao, Lin Yao

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Computational study of transcatheter aortic valve replacement based on patient-specific models—rapid surgical planning for self-expanding valves by Zhuangyuan Meng, Haishan Zhang, Yunhan Cai, Yuan Gao, Changbin Liang, Jun Wang, Xin Chen, Liang Guo, ShengZhang Wang, ShengZhang Wang

“…It also aimed to calculate the risks of postoperative paravalvular leak and atrioventricular conduction block, comparing these risks to clinical outcomes to verify the method’s effectiveness and accuracy. Based on medical images, six cases were established, including the aortic wall, native valve and calcification; one with a bicuspid aortic valve and five with tricuspid aortic valves. …”
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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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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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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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