Correction: Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer by Jing Yang, Huifen Ye, Xinjuan Fan, Yajun Li, Xiaomei Wu, Minning Zhao, Qingru Hu, Yunrui Ye, Lin Wu, Zhenhui Li, Xueli Zhang, Changhong Liang, Yingyi Wang, Yao Xu, Qian Li, Su Yao, Dingyun You, Ke Zhao, Zaiyi Liu

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Choosing the Optimal Spatial Domain Measure of Enhancement for Mammogram Images by Karen Panetta, Arash Samani, Sos Agaian

“…Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. …”
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Soft Tissue/Bone Decomposition of Conventional Chest Radiographs Using Nonparametric Image Priors by Yunbi Liu, Wei Yang, Guangnan She, Liming Zhong, Zhaoqiang Yun, Yang Chen, Ni Zhang, Liwei Hao, Zhentai Lu, Qianjin Feng, Wufan Chen

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Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer by Jing Yang, Huifen Ye, Xinjuan Fan, Yajun Li, Xiaomei Wu, Minning Zhao, Qingru Hu, Yunrui Ye, Lin Wu, Zhenhui Li, Xueli Zhang, Changhong Liang, Yingyi Wang, Yao Xu, Qian Li, Su Yao, Dingyun You, Ke Zhao, Zaiyi Liu

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Hopf Bifurcation in an SEIDQV Worm Propagation Model with Quarantine Strategy by Yu Yao, Wenlong Xiang, Andong Qu, Ge Yu, Fuxiang Gao

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Multiscale Image Registration by Dana Paquin, Doron Levy, Eduard Schreibmann, Lei Xing

“…A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. …”
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Optimizing Breast Cancer Mammogram Classification Through a Dual Approach: A Deep Learning Framework Combining ResNet50, SMOTE, and Fully Connected Layers for Balanced and Imbalanc... by Abdullah Fahad A. Alshamrani, Faisal Saleh Zuhair Alshomrani

“…Our approach significantly improves diagnostic accuracy in breast cancer classification from Mammogram scans, effectively addressing the challenge of imbalanced data in medical image analysis. This work contributes to medical image analysis and computer-aided diagnosis. …”
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A GPU-accelerated fuzzy method for real-time CT volume filtering. by Celia Tendero Delicado, Mónica Chillarón Pérez, Josep Arnal García, Vicent Vidal Gimeno, Esther Blanco Pérez

“…During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. …”
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Causality-driven candidate identification for reliable DNA methylation biomarker discovery by Xinlu Tang, Rui Guo, Zhanfeng Mo, Wenli Fu, Xiaohua Qian

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Optimizing the Locations and Sizes of Solar Assisted Electric Vehicle Charging Stations in an Urban Area by Dong Ji, Mingsong Lv, Jiayu Yang, Wang Yi

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Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation by Hossein Rabbani, Rahele Kafieh, Mahdi Kazemian Jahromi, Sahar Jorjandi, Alireza Mehri Dehnavi, Fedra Hajizadeh, Alireza Peyman

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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

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Towards an Optimal Energy Consumption for Unattended Mobile Sensor Networks through Autonomous Sensor Redeployment by Jian Chen, Jie Jia, Yingyou Wen, Dazhe Zhao

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Deep cascaded registration and weakly-supervised segmentation of fetal brain MRI by Valentin Comte, Mireia Alenya, Andrea Urru, Judith Recober, Ayako Nakaki, Francesca Crovetto, Oscar Camara, Eduard Gratacós, Elisenda Eixarch, Fatima Crispi, Gemma Piella, Mario Ceresa, Miguel A. González Ballester

“…While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…The speed-up process of detection and classification is crucial for effective cancer treatment. Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. …”
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From data to diagnosis: leveraging deep learning in IoT-based healthcare by Miracle A. Atianashie, Chukwuma Chinaza Adaobi

“…This article explores the advanced architectures of deep learning and their application in healthcare IoT, emphasizing the enhancement of disease prediction models, medical image analysis, and real-time health monitoring systems. …”
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A feature-based approach for atlas selection in automatic pelvic segmentation. by Guoping Shan, Xue Bai, Yun Ge, Binbing Wang

“…It highlighted the potential of feature-based subgrouping techniques in enhancing the efficacy of MAS algorithms in the field of medical image segmentation.…”
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Efficient Variational Approaches for Deformable Registration of Images by Mehmet Ali Akinlar, Muhammet Kurulay, Aydin Secer, Mustafa Bayram

“…Computational results indicate that the presented method is quite fast and efficient in the registration of deformable medical images.…”
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AutoLDP: An accurate and efficient artificial intelligence-based tool for automatic labeling of digital pathology by Yingnan Zhao, Huifen Ye, Jing Yang, Su Yao, Maohua Lv, Zhihong Chen, Yunrui Ye, Qingru Hu, Cheng Lu, Zaiyi Liu, Ke Zhao, Zhihua Chen

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Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions by Yuan Tian, Yongquan Liang, Yufeng Chen, Jingjing Zhang, Hongyang Bian

“…Abstract Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. …”
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