Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

“…Current methods to measure vascular density, however, are time-consuming, suffer from high inter-observer variability and are limited in describing the complex tumour vasculature morphometry. …”
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A hybrid model for detecting motion artifacts in ballistocardiogram signals by Yuelong Jiang, Han Zhang, Qizheng Zeng

“…Various methods, including filtering techniques and machine learning approaches, have been employed to address this issue, but the challenge persists due to the complexity and variability of motion artifacts. Methods This study introduces a hybrid model for detecting motion artifacts in ballistocardiogram (BCG) signals, utilizing a dual-channel approach. …”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…</p> <p>The generated patterns of global ET variability were relatively consistent across the ML models overall, but in regions with low data support via eddy covariance (EC) stations, we observed substantial biases across models and covariate setups and large ensemble uncertainties. …”
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Instance Segmentation of Sugar Apple (<i>Annona squamosa</i>) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model by Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang, Wenlong Wang

“…An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. …”
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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting by Ashkan Abbasi, PhD, Sowjanya Gowrisankaran, PhD, Wei-Chun Lin, MD, PhD, Xubo Song, PhD, Bhavna Josephine Antony, PhD, Gadi Wollstein, MD, Joel S. Schuman, MD, Hiroshi Ishikawa, MD

“…Hybrid-VF-Net exhibited greater resilience to data reliability issues, particularly in managing high false-negative rates often seen in moderate-to-severe glaucoma cases due to increased test–retest variability. Additionally, it demonstrated improved performance with fewer prior VF tests, thus reducing the waiting time needed for progression analysis. …”
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cigChannel: a large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation by G. Wang, G. Wang, G. Wang, X. Wu, X. Wu, X. Wu, W. Zhang, W. Zhang, W. Zhang

“…However, the synthetic seismic volumes in the <i>cigChannel</i> dataset still lack the variability and realism of field seismic data, potentially affecting the deep learning model's generalizability. …”
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Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…Future research should expand the diversity of data and refine sentiment recognition models to address cultural and environmental variability. By combining spatial indicators with emotional experiences, this research may contribute to the creation of inclusive, resilient and emotionally supportive child-friendly cities that prioritize safety and well-being.…”
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