Multi-Pathway 3D CNN With Conditional Random Field for Automated Segmentation of Multiple Sclerosis Lesions in MRI by Reeda Saeed, Shahab U. Ansari, Muhammad Hanif, Kamran Javed, Usman Haider, Iffat Maab, Saeed Mian Qaisar, Pawel Plawiak

“…One of the challenges in automatic MS lesion segmentation is the high variability of the lesion’s size and shape. In this work, a novel hybridization of the multi-scale features extraction, multi-pathway 3D convolutional neural network (CNN), and Conditional Random Field (CRF) is employed for an automated MS lesion detection and segmentation. …”
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Renal Biopsy Pathological Tissue Segmentation: A Comprehensive Review and Experimental Analysis by Yan Liu, Taiping Wang, Xinxin He, Wei Wang, Qiang He, Juan Jin

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Analysis and Prediction of Deformation of Shield Tunnel Under the Influence of Random Damages Based on Deep Learning by Xiaokai Niu, Yuqiang Pan, Wei Li, Zhitian Xie, Wei Song, Chengping Zhang

“…The results indicate that as the damage ratio increases, both the mean deformation and its variability progressively rise, leading to increased deformation instability, demonstrating the cumulative effect of damage on segment deformation. …”
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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…However, the complexity and variability of information flow in the brain require advanced analysis, especially if we consider the case of disrupted networks as those given by the brain connectome of stroke patients. …”
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Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM by LI Yunfeng, LAN Xiaosheng, SHEN Hongchang, XU Tongle

“…The fault diagnosis classification model outputs the highest classification accuracy of 100% after training, and the accuracy is better than the other five fault diagnosis models in the anti-noise experiment and the variable load experiment. The results show that the combination of convolutional neural network to extract fault features and parameters to optimize the classification model structure of support vector machine can not only improve the diagnostic accuracy, but also have strong generalization performance.…”
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COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings by Jordi Laguarta, Ferran Hueto, Brian Subirana

“…<italic>Methods:</italic> We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. …”
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Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach by Muhammad Khalid Hamid, Said Khalid Shah, Ghassan Husnain, Yazeed Yasin Ghadi, Shahab Ahmad Al Maaytah, Ayman Qahmash

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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

“…The plausibility of the predicted leaf angle time series was underlined by its close relationship with environmental variables related to transpiration. The evaluations confirm that AngleCam is a robust and efficient method to track leaf angles under field conditions. …”
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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…The CNN architecture includes two convolution layers, global max-pooling, and two fully connected layers, with 11 input variables and a single output for CS prediction. …”
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Improved deep learning for automatic localisation and segmentation of rectal cancer on T2‐weighted MRI by Zaixian Zhang, Junqi Han, Weina Ji, Henan Lou, Zhiming Li, Yabin Hu, Mingjia Wang, Baozhu Qi, Shunli Liu

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DreamOn: a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers by Luc Lerch, Luc Lerch, Lukas S. Huber, Lukas S. Huber, Amith Kamath, Alexander Pöllinger, Aurélie Pahud de Mortanges, Verena C. Obmann, Florian Dammann, Walter Senn, Walter Senn, Mauricio Reyes, Mauricio Reyes, Mauricio Reyes

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Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better? by Ahmed Soliman, Yalda Zafari-Ghadim, Yousif Yousif, Ahmed Ibrahim, Amr Mohamed, Essam A. Rashed, Mohamed A. Mabrok

“…Furthermore, we investigated the impact of an imbalanced distribution of the number of unconnected components in each slice, as a representation of common variability in stroke segmentation. Our findings reveal a potential robustness issue of Transformers to such variability, which may explain their unexpected weak performance. …”
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Land-Cover Semantic Segmentation for Very-High-Resolution Remote Sensing Imagery Using Deep Transfer Learning and Active Contour Loss by Miguel Chicchon, Francisco James Leon Trujillo, Ivan Sipiran, Ricardo Madrid

“…However, the automation of this process remains a challenge owing to the complexity of images, variability in land surface features, and noise. In this study, a method for training convolutional neural networks and transformers to perform land-cover segmentation on very-high-resolution aerial images in a regional context was proposed. …”
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Nondestructive egg freshness assessment using hyperspectral imaging and deep learning with distance correlation wavelength selection by Pauline Ong, Shih-Yen Chiu, I-Lin Tsai, Yen-Chou Kuan, Yu-Jen Wang, Yung-Kun Chuang

“…Spectral data were preprocessed using standard normal variates to minimize spectral variability, followed by wavelength selection - a crucial step for improving model predictability. …”
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Deep Learning Architecture for Tomato Plant Leaf Detection in Images Captured in Complex Outdoor Environments by Andros Meraz-Hernández, Jorge Fuentes-Pacheco, Andrea Magadán-Salazar, Raúl Pinto-Elías, Nimrod González-Franco

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Hybrid CNN-Transformer Architecture With Xception-Based Feature Enhancement for Accurate Breast Cancer Classification by Alireza Zeynali, Mohammad Ali Tinati, Behzad Mozaffari Tazehkand

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The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review by Jingguo Qu, Xinyang Han, Man-Lik Chui, Yao Pu, Simon Takadiyi Gunda, Ziman Chen, Jing Qin, Ann Dorothy King, Winnie Chiu-Wing Chu, Jing Cai, Michael Tin-Cheung Ying

“…Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. …”
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A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island by Pavlos Fylaktos, George P. Petropoulos, Ioannis Lemesios

“…The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. …”
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End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems by Chi Xu, Xinyi Du, Lin Li, Xinchun Li, Haibin Yu

“…Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. …”
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AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model by Mojtaba Sirati-Amsheh, Elham Shabaninia, Ali Chaparian

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