An Automatic Method for Interest Point Detection by I. G. Zubov

“…The availability of digital cameras and large sets of annotated data, as well as the power of modern computing technologies, render monocular image analysis a dynamically developing direction in the field of machine vision. …”
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Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach by Ju Eun Cho, Se Yeon Kang, Yi Yeon Hong, Han Jong Jun

“…Two convolutional neural network long short-term memory deep learning models were trained on the EEG data: one using all the ERP segments, and the other focusing on statistically significant ERP features. …”
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Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease by Chang Hu, Yihong Dong, Shoubo Peng, Yuehan Wu

“…Due to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. …”
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Lightweight patch-level attention for efficient pig behavior detection: A novel dataset and approach by Shuo Wan, Zhongqiang Huang, Tao Han, Ying Sha

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A Comprehensive Joint Learning System to Detect Skin Cancer by Lubna Riaz, Hafiz Muhammad Qadir, Ghulam Ali, Mubashir Ali, Muhammad Ahsan Raza, Anca D. Jurcut, Jehad Ali

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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

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Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning by Pierre Gouton, Kacoutchy Jean Ayikpa, Diarra Mamadou

“…These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. …”
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Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography by Yujie Ma, Maged Ali Al-Aroomi, Yutian Zheng, Wenjie Ren, Peixuan Liu, Qing Wu, Ye Liang, Canhua Jiang

“…For the verification of tooth recognition, the data from the validation set were randomly selected for analysis. …”
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Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology by Petr Kuritcyn, Rosalie Kletzander, Sophia Eisenberg, Thomas Wittenberg, Volker Bruns, Katja Evert, Felix Keil, Paul K. Ziegler, Katrin Bankov, Peter Wild, Markus Eckstein, Arndt Hartmann, Carol I. Geppert, Michaela Benz

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NetBat: A network-driven harmonization method for structural connectivity by Gustav R. Sjobeck, Mahbaneh Eshaghzadeh Torbati, Davneet S. Minhas, Charles S. DeCarli, James D. Wilson, Dana L. Tudorascu

“…As the practice of aggregating multi-site neuroimaging data has become more common, the field of neuroscience has increasingly recognized the importance of harmonization , or the removal of scanner effects from brain imaging data. …”
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Supervised Semantic Segmentation of Urban Area Using SAR by Joanna Pluto-Kossakowska, Sandhi Wangiyana

“…We present a novel dataset for urban classification focusing on density analysis. Machine learning methods, including a selection of artificial neural networks and other classifiers, have been used to distinguish among different classes of built-up areas, as defined according to the Urban Atlas database. …”
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PatchOut: A novel patch-free approach based on a transformer-CNN hybrid framework for fine-grained land-cover classification on large-scale airborne hyperspectral images by Renjie Ji, Kun Tan, Xue Wang, Shuwei Tang, Jin Sun, Chao Niu, Chen Pan

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Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis by Junjie Ma, Fang Yang, Rong Yang, Yuan Li, Yongjing Chen

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Quantification of CO₂ hotspot emissions from OCO-3 SAM CO₂ satellite images using deep learning methods by J. Dumont Le Brazidec, J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, G. Broquet, G. Kuhlmann, M. Bocquet

“…We present an end-to-end convolutional neural network (CNN) approach, processing the satellite XCO<span class="inline-formula">₂</span> images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume owing to the limited extent of the satellite swath.…”
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White-Matter Connectivity and General Movements in Infants with Perinatal Brain Injury by Ellen N. Sutter, Jose Guerrero-Gonzalez, Cameron P. Casey, Douglas C. Dean, Andrea de Abreu e Gouvea, Colleen Peyton, Ryan M. McAdams, Bernadette T. Gillick

“…Tractography was used to identify the corticospinal tract, a key motor pathway often affected by perinatal brain injury, and tract-based spatial statistics (TBSS) were used to examine broader white matter networks. Diffusion parameters from the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models were compared between infants with and without typical general movements. …”
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Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female b... by Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker

“…The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. …”
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Detecting soil-transmitted helminth and Schistosoma mansoni eggs in Kato-Katz stool smear microscopy images: A comprehensive in- and out-of-distribution evaluation of YOLOv7 varian... by Mohammed Aliy Mohammed, Esla Timothy Anzaku, Peter Kenneth Ward, Bruno Levecke, Janarthanan Krishnamoorthy, Wesley De Neve, Sofie Van Hoecke

“…However, their performance in real-world, out-of-distribution (OOD) scenarios, characterized by variations in image capture devices and the appearance of previously unseen egg types, has not been thoroughly investigated. …”
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Large-scale self-normalizing neural networks by Zhaodong Chen, Weiqin Zhao, Lei Deng, Yufei Ding, Qinghao Wen, Guoqi Li, Yuan Xie

“…Self-normalizing neural networks (SNN) regulate the activation and gradient flows through activation functions with the self-normalization property. …”
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Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG by Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin

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Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process by Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata

“…The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods, demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring.…”
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