Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

“…We propose to incorporate auxiliary loss functions derived from high-level vision tasks, including text detection using the connectionist text proposal network (CTPN), text recognition via a convolutional recurrent neural network (CRNN), keypoints localization using Key.Net, and hue consistency. …”
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Enhancing chronic wound assessment through agreement analysis and tissue segmentation by Ana C. Morgado, Rafaela Carvalho, Ana Filipa Sampaio, Maria J. M. Vasconcelos

“…In this work, inter-rater agreement analyses were conducted to evaluate the consistency of manual annotations performed by multiple experts and an automated methodology for tissue segmentation leveraging advanced deep learning techniques is proposed. For this, the convolutional neural network DeepLabV3-R50 and a transformer-based approach (SegFormer-B0) were explored. …”
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AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…Classical ML models outperformed most DL architectures, including Transformer and Convolutional Neural Network (CNN)-LSTM, which underperformed despite their complexity. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…Statistical tests, including the Friedman and Nemenyi post-hoc test, identified the CNN model trained with MHMXAI-selected features as the most robust choice for CKD stage prediction. …”
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A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in by Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi

“…The pipeline integrates a fine-tuned Mask Region-based Convolutional Neural Network (Mask R-CNN) segmentation model trained on 176 plant images and achieves high performance despite the small training data set (Dice coefficient = 0.781). …”
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Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000 by Yabin Zhang, Lei Yu, Yuting Lv, Tiantian Yang, Qi Guo

“…Results reveal exponential growth post-2017, driven by advancements in deep learning and multimodal data integration. …”
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Multi-Conv attention network for skin lesion image segmentation by Zexin Li, Hanchen Wang, Haoyu Chen, Chenxin Lin, Aochen Yan

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Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system by Adi Wibowo, Leni Sophia Heliani, Cecep Pratama, David Prambudi Sahara, Sri Widiyantoro, Dadan Ramdani, Mizan Bustanul Fuady Bisri, Ajat Sudrajat, Sidik Tri Wibowo, Satriawan Rasyid Purnama

“…We have integrated several post-processing techniques to further bolster the model's robustness against noise. …”
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An efficient sparse pruning method for human pose estimation by Mingyang Wang, Tianyi Sun, Kang Song, Shuang Li, Jing Jiang, Linjun Sun

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Advanced phenotyping in tomato fruit classification through artificial intelligence by Sandra Eulália Santos Faria, Alcinei Místico Azevedo, Nayany Gomes Rabelo, Varlen Zeferino Anastácio, Valentina de Melo Maciel, Deltimara Viana Matos, Elias Barbosa Rodrigues, Phelipe Souza Amorim, Janete Ramos da Silva, Fernanda de Souza Santos

“…Recent advances in the field of computational resources, such as image phenotyping have enabled pre- and post-harvest assessments that are both fast and precise. …”
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Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R² of 0.857, 0.910, and 0.963, respectively. …”
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Low scaling factor Seam Carving tamper detection algorithm with hybrid attention by ZHAO Jie, CHANG Haochan, WU Bin

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A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems by Shuaibo Wang, Xinyuan Xiang, Jie Zhang, Zhuohang Liang, Shufang Li, Peilin Zhong, Jie Zeng, Chenguang Wang

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Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females by Saied Ramedani, Ebru Kelesoglu, Norman Stutzig, Hendrik Von Tengg‐Kobligk, Keivan Daneshvar Ghorbani, Tobias Siebert

“…CG]) × 2 (TIME [pre‐intervention vs. post‐intervention]) analysis of variance (ANOVA) with repeated measures for the factor TIME. …”
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Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

“…To detect glaucoma progression from ocular hypertension both structurally and functionally, and identify potential objective early biomarkers associated with progression, we developed and evaluated deep convolutional long short-term memory (CNN-LSTM) neural network models using longitudinal CFPs from the Ocular Hypertension Treatment Study (OHTS). …”
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Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature by Bosen Peng, Jiancheng Mu, Feng Xu, Wanyue Guo, Chuhuan Sun, Wei Fan

“…Key research hot spots are identified by keywords such as “deep learning,” “machine learning,” “convolutional neural network,” ”diabetic retinopathy,“ and ”ophthalmology.…”
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An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body r... by Yi Chen, David Pasquier, Damon Verstappen, Henry C. Woodruff, Philippe Lambin

“…Deep learning models, leveraging various convolutional neural networks (CNNs), were employed to effectively integrate both image and clinical data. …”
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Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

“…As the target of this study is not to propose a new image segmentation model, the existing medical imaging segmentation models—including U-Net, ResUNet++, Fully Convolutional Network, and a modified algorithm based on the Conditional Bernoulli Diffusion Model—are used. …”
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EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…Second, 1D Convolutional Neural Networks models are developed, and pre-processed EEG signals are fed as input. …”
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Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors by Muhammad Bilal, Muhammad Shehzad Hanif

“…In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency. …”
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