A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study by Rui-Tao Sun, Chang-Lei Li, Yu-Min Jiang, Ao-Yun Hao, Kui Liu, Kun Li, Bin Tan, Xiao-Nan Yang, Jiu-Fa Cui, Wen-Ye Bai, Wei-Yu Hu, Jing-Yu Cao, Chao Qu

“…Methods A retrospective analysis was conducted on 2,055 patients who underwent laparoscopic cholecystectomy (LC) for cholecystitis at our center. Preoperative CT images were processed with super-resolution reconstruction to improve consistency, and high-throughput radiomic features were extracted from the gallbladder wall region. …”
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Overheating Defect Detection of Composite Insulator Based on Mask R-CNN by Yi GAO, Lianfang TIAN, Qiliang DU

“…The results show that the algorithm proposed in this paper has a high detection accuracy of 100% for the infrared images of composite insulators with serious and urgent defects, but has false detection occurrence for the infrared images without overheating defects or with general defects. …”
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Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear arrayScienceDB by Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon

“…To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. …”
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Evaluating the Effect of Co-Registered Diagnostic MR Images Based CT Simulation on Target Volume Delineation and Dose Distribution for Tomotherapy of Rectal Cancer by Baranoosh Rahmani, Daryoush Shahbazi-Gahrouei, Mahnaz Roayaei

“…Following the co-registration process with the deformable algorithm, target volumes are defined. …”
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A Framework for Constructing Large-Scale Dynamic Datasets for Water Conservancy Image Recognition Using Multi-Role Collaboration and Intelligent Annotation by Xueying Song, Xiaofeng Wang, Ganggang Zuo, Jiancang Xie

“…Two supporting tools were developed: an image classification modification tool that automatically adapts to changes in categories and an automatic annotation tool with rotation-angle perception based on the rotation matrix algorithm. …”
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Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging by Randy Guzmán Gómez, Guadalupe Lopez Lopez, Victor M. Alvarado, Froylan Lopez Lopez, Eréndira Esqueda Cisneros, Hazel López Moreno

“…The rapid growth of artificial intelligence, particularly in the field of deep learning, has opened up new advances in analyzing and processing large and complex datasets. Prospects and emerging trends in this area engage the development of methods, techniques, and algorithms to build autonomous systems that perform tasks with minimal human action. …”
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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…The experimental setup utilized an ultrahigh-sensitivity Talbot-Lau interferometer operated with a conventional X-ray tube to generate scattering images, which were processed using a Fourier Transform-based algorithm. …”
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Two-Dimensional Spatial Variation Analysis and Correction Method for High-Resolution Wide-Swath Spaceborne Synthetic Aperture Radar (SAR) Imaging by Zhenyu Hou, Pin Li, Zehua Zhang, Zhuo Yun, Feng He, Zhen Dong

“…However, as the resolution increases and the swath widens, the two-dimensional (2D) spatial variation between different targets in the scene and the radar becomes very pronounced, severely affecting the high-precision focusing and high-quality imaging of spaceborne SAR. In previous studies on the correction of two-dimensional spatial variation in spaceborne SAR, either the models were not accurate enough or the computational efficiency was low, limiting the application of corresponding algorithms. …”
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GROG Facilitated Compressed Sensing for Radial MRI by Yumna Bilal, Ibtisam Aslam, Muhammad Faisal Siddiqui, Omair Inam, Kashif Amjad, Jawad Hasan Alkhateeb, Hammad Omer

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An Adaptive YOLO11 Framework for the Localisation, Tracking, and Imaging of Small Aerial Targets Using a Pan–Tilt–Zoom Camera Network by Ming Him Lui, Haixu Liu, Zhuochen Tang, Hang Yuan, David Williams, Dongjin Lee, K. C. Wong, Zihao Wang

“…This article presents a cost-effective camera network system that employs neural network-based object detection and stereo vision to assist a pan–tilt–zoom camera in imaging fast, erratically moving small aerial targets. …”
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A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging by Joy Chakra Bortty, Gouri Shankar Chakraborty, Inshad Rahman Noman, Salil Batra, Joy Das, Kanchon Kumar Bishnu, Md Tanvir Rahman Tarafder, Araf Islam

“…<b>Methods:</b> A powerful vision transformer model (ViT-B16) with three efficient Convolutional Neural Network (CNN) models (VGG19, ResNet152V2, and EfficientNetV2B3) has been trained with a benchmark dataset, ‘OASIS’, that comes with a high volume of brain Magnetic Resonance Images (MRI). <b>Results:</b> A weighted average ensemble technique with a Grasshopper optimization algorithm has been designed and utilized to ensure maximum performance with high accuracy of 97.31%, precision of 97.32, recall of 97.35, and F1 score of 0.97. …”
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LUNGINFORMER: A Multiclass of lung pneumonia diseases detection based on chest X-ray image using contrast enhancement and hybridization inceptionresnet and transformer by Hanafi Hanafi

“…The majority of lung pneumonia diseases are diagnosed using traditional medical tools and specialized medical personnel. This process is both time-consuming and expensive. To address the problem, many researchers have employed deep learning algorithms to develop an automated detection system for pneumonia. …”
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A hyperspectral approach for retrieving inherent optical properties, phytoplankton pigments, and associated uncertainties from non-water absorption by Brice Grunert, Audrey Ciochetto, Audrey Ciochetto, Colleen Mouw

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Exposed conductor detection of 10 kV distribution line based on improved YOLOv8 by Qiwen JING, Sipeng HAO, Siyuan LI

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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of white button mushrooms (Agaricus bisporus) for MC prediction. …”
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AGU2-Net: Multi-Scale U²-Net Enhanced by Attention Gate Mechanism for Image Tampering Localization by Kefei Wu, Lin Li, Qingyan Li

“…Although deep learning algorithms based on convolutional neural networks (CNNs) have made notable progress in image forgery detection, they still face certain limitations in effectively detecting and localizing tampered areas due to the subtle nature of existing manipulation traces. …”
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A METHOD OF ELIMINATING MALICIOUS PERTURBATIONS EMBEDDED BY L0-OPTIMIZED ATTACKS by Dmitry A. Yesipov, Nikita S. Sulimenko, Ilya Yu. Popov

“…Each perturbed pixel is processed sequentially. The restored image is being sent to the input of the protected model. …”
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Super-Resolution Correlating Optical Endoscopy by Oskar Tamm, Vipin Tiwari, Shivasubramanian Gopinath, Aravind Simon John Francis Rajeswary, Scott Arockia Singh, Joseph Rosen, Vijayakumar Anand

“…The 2D matrices of the object and the PSF were processed using a deconvolution algorithm to reconstruct a super-resolution image of the object. …”
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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

“…However, manual segmentation of CTA images is labor intensive and time consuming. Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders by Ziqin Yang, Ziqin Yang, Nicole A. Teaney, Nicole A. Teaney, Elizabeth D. Buttermore, Elizabeth D. Buttermore, Elizabeth D. Buttermore, Mustafa Sahin, Mustafa Sahin, Mustafa Sahin, Wardiya Afshar-Saber, Wardiya Afshar-Saber

“…Recent progress in machine learning (ML) algorithms also presents unprecedented opportunities to advance the NDD research process. …”
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