A versatile deep learning architecture for classification and label-free prediction of hyperspectral images

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Image-to-Image Translation with Conditional Adversarial Networks

Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

FSIM: A Feature Similarity Index for Image Quality Assessment

Zhang, L., Lei Zhang, A., Mou, X., & Zhang, D. D. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2109730 · Cited by 5,220 · see citing works

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2584107 · Cited by 3,005 · see citing works

Deep Learning for Hyperspectral Image Classification: An Overview

Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007 · see citing works

HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification

Roy, S. K., Krishna, G., Dubey, S. R., & Chaudhuri, B. B. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2918719 · Cited by 1,926 · see citing works

U-Net: deep learning for cell counting, detection, and morphometry

Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0261-2 · Cited by 1,912 · see citing works

Deep Recurrent Neural Networks for Hyperspectral Image Classification

Mou, L., Ghamisi, P., & Zhu, X. X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2636241 · Cited by 1,397 · see citing works

ScanImage: Flexible software for operating laser scanning microscopes

Pologruto, T. A., Sabatini, B. L., & Svoboda, K. (2003) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-2-13 · Cited by 1,297 · see citing works

Content-aware image restoration: pushing the limits of fluorescence microscopy

WEIGERT, M. G., Schmidt, U., Boothe, T., Müller, A., Dibrov, A., Jain, A., Wilhelm, B. G., Schmidt, D., Broaddus, C., Culley, S., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0216-7 · Cited by 1,210 · see citing works

Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2129595 · Cited by 1,158 · see citing works

Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine

Cheng, J.-X., & Xie, X. S. (2015) · Science · doi:10.1126/science.aaa8870 · Cited by 874 · see citing works

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

Hang, R., Liu, Q., Hong, D., & Ghamisi, P. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2899129 · Cited by 841 · see citing works

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning

Rivenson, Y., Wang, H., Wei, Z., Haan, K. D., Zhang, Y., Wu, Y., Günaydın, H., Zuckerman, J. E., Chong, T., Sisk, A. E. J., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0362-y · Cited by 653 · see citing works

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Christiansen, E. M., Yang, S. J., Ando, D. M., Javaherian, A., Skibinski, G., Lipnick, S., Mount, E., O’Neil, A., Shah, K., Lee, A. K., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.040 · Cited by 635 · see citing works

Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction

Belthangady, C., & Royer, L. A. (2019) · Nature Methods · doi:10.1038/s41592-019-0458-z · Cited by 502 · see citing works

FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry

Palmer, A., Phapale, P. B., Chernyavsky, I., Lavigne, R., Fay, D., Tarasov, A., Kovalev, V., Fuchser, J., Nikolenko, S., Pineau, C., et al. (2016) · Nature Methods · doi:10.1038/nmeth.4072 · Cited by 499 · see citing works

Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy

Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F., & Johnson, G. R. (2018) · Nature Methods · doi:10.1038/s41592-018-0111-2 · Cited by 495 · see citing works

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

Cao, X., Zhou, F., Xu, L., Meng, D., Xu, Z., & Paisley, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2799324 · Cited by 331 · see citing works

Hyperspectral Imaging with Stimulated Raman Scattering by Chirped Femtosecond Lasers

Fu, D., Holtom, G., Freudiger, C. W., Zhang, X., & Xie, X. S. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp308938t · Cited by 320 · see citing works

220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3

Baumgardner, M. F., Biehl, L. L., & Landgrebe, D. A. (2015) · doi:10.4231/r7rx991c · Cited by 313 · see citing works

An Active Learning Approach to Hyperspectral Data Classification

Rajan, S., Ghosh, J., & Crawford, M. M. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.910220 · Cited by 292 · see citing works

On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery

Zhao, W., Guo, Z., Yue, J., Zhang, X., & Luo, L. (2015) · International Journal of Remote Sensing · doi:10.1080/2150704x.2015.1062157 · Cited by 194 · see citing works

Label-Free Live-Cell Imaging with Confocal Raman Microscopy

Klein, K., Gigler, A. M., Aschenbrenner, T., Monetti, R., Bunk, W., Jamitzky, F., Morfill, G. E., Stark, R. W., & Schlegel, J. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2011.12.027 · Cited by 171 · see citing works

Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy

Zhang, L., Wu, Y., Zheng, B., Su, L., Chen, Y., Ma, S., Hu, Q., Zou, X., Yao, L., Yang, Y., et al. (2019) · Theranostics · doi:10.7150/thno.32655 · Cited by 159 · see citing works

Hyperspectral phasor analysis enables multiplexed 5D in vivo imaging

Cutrale, F., Trivedi, V., Trinh, L. A., Chiu, C., Choi, J. M., Artiga, M. S., & Fraser, S. E. (2017) · Nature Methods · doi:10.1038/nmeth.4134 · Cited by 158 · see citing works

BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Santara, A., Mani, K., Hatwar, P., Singh, A., Garg, A., Padia, K., & Mitra, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2705073 · Cited by 148 · see citing works

Hyperspectral image classification via contextual deep learning

Ma, X., Geng, J., & Wang, H. (2015) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-015-0071-8 · Cited by 136 · see citing works

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

Inglese, P., McKenzie, J. S., Mróz, A., Kinross, J. K., Veselkov, K., Holmes, E. C., Takáts, Z., Nicholson, J. K., & Glen, R. C. (2017) · Chemical Science · doi:10.1039/c6sc03738k · Cited by 136 · see citing works

Deep learning for tumor classification in imaging mass spectrometry

Behrmann, J., Etmann, C., Boskamp, T., Casadonte, R., Kriegsmann, J., & Maaß, P. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx724 · Cited by 127 · see citing works

Denoising of stimulated Raman scattering microscopy images via deep learning

Manifold, B., Thomas, E. C., Francis, A. T., Hill, A. H., & Fu, D. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.003860 · Cited by 125 · see citing works

Reliable Cell Segmentation Based on Spectral Phasor Analysis of Hyperspectral Stimulated Raman Scattering Imaging Data

Fu, D., & Xie, X. S. (2014) · Analytical Chemistry · doi:10.1021/ac500014b · Cited by 124 · see citing works

Hyperspectral light sheet microscopy

Jahr, W., Schmid, B., Schmied, C., Fahrbach, F. O., & Huisken, J. (2015) · Nature Communications · doi:10.1038/ncomms8990 · Cited by 123 · see citing works

Hyperspectral Visualization of Mass Spectrometry Imaging Data

Fonville, J. M., Carter, C. L., Pizarro, L., Steven, R. T., Palmer, A., Griffiths, R. L., Lalor, P. F., Lindon, J. C., Nicholson, J. K., Holmes, E. C., et al. (2012) · Analytical Chemistry · doi:10.1021/ac302330a · Cited by 123 · see citing works

Hybrid first and second order attention Unet for building segmentation in remote sensing images

He, N., Fang, L., & Plaza, A. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2791-7 · Cited by 119 · see citing works

相干拉曼散射显微术

Xie, X., Zhang, X., Chen, T., Yu, Z., & HUANG, Y. (2012) · Scientia Sinica Chimica · doi:10.1360/032011-609 · Cited by 118 · see citing works

High‐speed MALDI MS/MS imaging mass spectrometry using continuous raster sampling

Prentice, B. M., Chumbley, C. W., & Caprioli, R. M. (2015) · Journal of Mass Spectrometry · doi:10.1002/jms.3579 · Cited by 107 · see citing works

Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks

Bayramoğlu, N., Kaakinen, M., Eklund, L., & Heikkilä, J. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.15 · Cited by 102 · see citing works

A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets

Mukherjee, P., Zhou, M., Lee, E. H., Schicht, A., Balagurunathan, Y., Napel, S. A., Gillies, R. J., Wong, S. S., Thieme, A. H., Leung, A. N. C., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0173-6 · Cited by 99 · see citing works

Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks

Berisha, S., Lotfollahi, M., Jahanipour, J., Gurcan, I., Walsh, M. J., Bhargava, R., Nguyen, H. V., & Mayerich, D. M. (2018) · The Analyst · doi:10.1039/c8an01495g · Cited by 97 · see citing works

Teaching image-processing programming in Java

Sage, D., & Unser, M. A. (2003) · IEEE Signal Processing Magazine · doi:10.1109/msp.2003.1253553 · Cited by 96 · see citing works

Hyperspectral image analysis using deep learning — A review

Petersson, H., Gustafsson, D. K. J., & Bergstrom, D. (2016) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2016.7820963 · Cited by 95 · see citing works

Support vector machines for classification of hyperspectral remote-sensing images

Melgani, F., & Bruzzone, L. (2003) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2002.1025088 · Cited by 95 · see citing works

Cellular Imaging Using Stimulated Raman Scattering Microscopy

Hill, A. H., & Fu, D. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02095 · Cited by 93 · see citing works

Automated abnormality detection in lower extremity radiographs using deep learning

Varma, M., Lu, M., Gardner, R., Dunnmon, J., Khandwala, N., Rajpurkar, P., Long, J., Beaulieu, C. F., Shpanskaya, K. S., Fei-Fei, L., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0126-0 · Cited by 89 · see citing works

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis

Rana, A., Lowe, A. C.-Y., Lithgow, M. Y., Horback, K. L., Janovitz, T., da Silva, A., Tsai, H. K., Shanmugam, V., Bayat, A., & Shah, P. P. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.5111 · Cited by 89 · see citing works

Improving healthcare operations management with machine learning

Pianykh, O. S., Guitron, S., Parke, D., Zhang, C., Pandharipande, P., Brink, J., & Rosenthal, D. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0176-3 · Cited by 87 · see citing works

Stain-less staining for computed histopathology

Mayerich, D. M., Walsh, M. J., Kadjacsy-Balla, A., Ray, P. S., Hewitt, S. M., & Bhargava, R. (2015) · TECHNOLOGY · doi:10.1142/s2339547815200010 · Cited by 75 · see citing works

MALDI-MSI of Immunotherapy: Mapping the EGFR-Targeting Antibody Cetuximab in 3D Colon-Cancer Cell Cultures

Liu, X., Lukowski, J. K., Flinders, C., Kim, S., Georgiadis, R. A., Mumenthaler, S. M., & Hummon, A. B. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b02151 · Cited by 56 · see citing works

Testing for presence of known and unknown molecules in imaging mass spectrometry

Alexandrov, T., & Bartels, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt388 · Cited by 55 · see citing works

High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering

Zhang, J., Zhao, J., Lin, H., Tan, Y., & Cheng, J.-X. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c01598 · Cited by 54 · see citing works

Quantitative chemical imaging with stimulated Raman scattering microscopy

Fu, D. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2017.05.002 · Cited by 53 · see citing works

Cell classification using convolutional neural networks in medical hyperspectral imagery

Xiang, L., Li, W., Xu, X., & Hu, W. (2017) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc.2017.7984606 · Cited by 52 · see citing works

Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification

Meng, Z., Li, L., Tang, X., Feng, Z., Jiao, L., & Liang, M. (2019) · Remote Sensing · doi:10.3390/rs11161896 · Cited by 45 · see citing works

M-UNet: Modified U-Net Segmentation Framework with Satellite Imagery

Soni, A., Koner, R., & Villuri, V. G. K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2188-1_4 · Cited by 43 · see citing works

Digital Assessment of Stained Breast Tissue Images for Comprehensive Tumor and Microenvironment Analysis

Mittal, S., Stoean, C., Kajdacsy‐Balla, A., & Bhargava, R. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00246 · Cited by 38 · see citing works

EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data

Wijetunge, C. D., Saeed, I., Boughton, B. A., Spraggins, J. M., Caprioli, R. M., Bacic, A., Roessner, U., & Halgamuge, S. K. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv356 · Cited by 35 · see citing works

Computational modeling of cellular structures using conditional deep generative networks

Yuan, H., Cai, L., Wang, Z., Hu, X., Zhang, S., & Ji, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty923 · Cited by 33 · see citing works

Developing a Drug Screening Platform: MALDI-Mass Spectrometry Imaging of Paper-Based Cultures

Tobias, F., McIntosh, J. C., LaBonia, G. J., Boyce, M. W., Lockett, M. R., & Hummon, A. B. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b03536 · Cited by 32 · see citing works

Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image ClassificationPreprint

Luo, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12563 · Cited by 23 · see citing works

Enhancing classification of mass spectrometry imaging data with deep neural networks

Thomas, S. A., Jin, Y., Bunch, J., & Gilmore, I. S. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285223 · Cited by 22 · see citing works

SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data

Inglese, P., Correia, G. D. S., Takáts, Z., Nicholson, J. K., & Glen, R. C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty622 · Cited by 22 · see citing works

A general generative adversarial capsule network for hyperspectral image spectral-spatial classification

Xue, Z. (2019) · Remote Sensing Letters · doi:10.1080/2150704x.2019.1681598 · Cited by 22 · see citing works

Hyperspectral Image Classification With Deep Metric Learning and Conditional Random Field

Liang, Y., Zhao, X. W., Guo, A. J. X., & Zhu, F. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2939356 · Cited by 19 · see citing works

Clusterwise Peak Detection and Filtering Based on Spatial Distribution To Efficiently Mine Mass Spectrometry Imaging Data

Eriksson, J. O., Rezeli, M., Hefner, M., Marko‐Varga, G., & Horvatovich, P. L. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02637 · Cited by 12 · see citing works

WetlandNet: Semantic Segmentation for Remote Sensing Images of Coastal Wetlands via Improved UNet with Deconvolution

Cui, B., Zhang, Y., Li, X., Wu, J., & Lu, Y. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-3308-2_32 · Cited by 10 · see citing works

Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine

Shahraki, F. F., Saadatifard, L., Berisha, S., Lotfollahi, M., Mayerich, D. M., & Prasad, S. (2020) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-38617-7_4 · Cited by 4 · see citing works

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Qiu, S., Cai, Y., Yao, H., Lin, C., Xie, Y., Tang, S., & Zhang, A. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01399-3 · Cited by 939 · see citing works

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy

Qian, N., Gao, X., Lang, X., Deng, H., Bratu, T. M., Chen, Q., Stapleton, P., Yan, B., & Min, W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300582121 · Cited by 467 · see citing works

Hyperspectral imaging and its applications: A review

Bhargava, A., Sachdeva, A., Sharma, K., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33208 · Cited by 293 · see citing works

HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model

Wang, D., Hu, M., Jin, Y., Miao, Y., Yang, J., Xu, Y., Qin, X., Ma, J., Sun, L., Li, C., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3557581 · Cited by 229 · see citing works

Deep learning in spectral analysis: Modeling and imaging

Liu, X., An, H., Cai, W., & Shao, X. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117612 · Cited by 143 · see citing works

Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology

Liu, Z., Su, W., Ao, J., Wang, M., Jiang, Q., He, J., Gao, H., Lei, S., Nie, J., Yan, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31339-8 · Cited by 135 · see citing works

Recent Developments in Machine Learning for Mass Spectrometry

Beck, A. G., Muhoberac, M., Randolph, C. E., Beveridge, C. H., Wijewardhane, P. R., Kenttämaa, H. I., & Chopra, G. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00060 · Cited by 144 · see citing works

Deep Learning in Medical Hyperspectral Images: A Review

Cui, R., Yu, H., Xu, T., Xing, X., Cao, X., Yan, K., & Chen, J. (2022) · Sensors · doi:10.3390/s22249790 · Cited by 116 · see citing works

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza, A., Siddiqui, H. U. R., Munir, K., Almutairi, M. S., Rustam, F., & Ashraf, I. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276525 · Cited by 111 · see citing works

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Liu, Z., Chen, L., Cheng, H., Ao, J., Xiong, J., Liu, X., Chen, Y., Mao, Y. Q., & Ji, M. (2024) · Science Advances · doi:10.1126/sciadv.adn3426 · Cited by 83 · see citing works

Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches

Cai, Z., Huang, Z., He, M., Li, C., Qi, H., Peng, J., Zhou, F., & Zhang, C. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2023.136169 · Cited by 82 · see citing works

Positive-unlabeled learning in bioinformatics and computational biology: a brief review

Li, F., Dong, S., Leier, A., Han, M., Guo, X., Xu, J., Wang, X., Pan, S., Jia, C., Zhang, Y., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab461 · Cited by 81 · see citing works

Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls

Manifold, B., & Fu, D. (2022) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-061020-015110 · Cited by 62 · see citing works

Deep learning for retrieving omni-directional ocean wave spectra from spaceborne synthetic aperture radar

Wu, K., & Li, X. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114386 · Cited by 61 · see citing works

Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science

Lin, H., & Cheng, J. (2023) · eLight · doi:10.1186/s43593-022-00038-8 · Cited by 60 · see citing works

Digital image correlation based on convolutional neural networks

Duan, X., Xu, H., Dong, R., Lin, F., & Huang, J. (2022) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2022.107234 · Cited by 58 · see citing works

Machine Learning and Deep Learning Techniques for Spectral Spatial Classification of Hyperspectral Images: A Comprehensive Survey

Grewal, R., Kasana, S. S., & Kasana, G. (2023) · Electronics · doi:10.3390/electronics12030488 · Cited by 58 · see citing works

Theory, innovations and applications of stimulated Raman scattering microscopy

Min, W., Cheng, J., & Ozeki, Y. (2025) · Nature Photonics · doi:10.1038/s41566-025-01707-z · Cited by 58 · see citing works

MnMOF-based microwave-glutathione dual-responsive nano-missile for enhanced microwave Thermo-dynamic chemotherapy of drug-resistant tumors

Wu, Q., Du, Q., Sun, X., Niu, M., Tan, L., Fu, C., Ren, X., Zheng, Y., Liang, T., Zhao, J., et al. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.135582 · Cited by 57 · see citing works

Flex-DLD: Deep Low-Rank Decomposition Model With Flexible Priors for Hyperspectral Image Denoising and Restoration

Chen, Y., Zhang, H., Wang, Y., Yang, Y., & Wu, Q. M. J. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3360902 · Cited by 57 · see citing works

AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification

Li, W., Lambert-Garcia, R., Getley, A. C. M., Kim, K., Bhagavath, S., Majkut, M., Rack, A., Lee, P. D., & Leung, C. L. A. (2024) · Virtual and Physical Prototyping · doi:10.1080/17452759.2024.2325572 · Cited by 55 · see citing works

Metasurface‐Enhanced Mid‐Infrared Spectrochemical Imaging of Tissues

Rosas, S., Schoeller, K. A., Chang, E., Mei, H., Kats, M. A., Eliceiri, K. W., Zhao, X., & Yesilköy, F. (2023) · Advanced Materials · doi:10.1002/adma.202301208 · Cited by 54 · see citing works

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Georgiev, D., Fernández-Galiana, Á., Vilms Pedersen, S., Papadopoulos, G., Xie, R., Stevens, M. M., & Barahona, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2407439121 · Cited by 50 · see citing works

Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy

He, H., Cao, M., Gao, Y., Zheng, P., Yan, S., Zhong, J., Wang, L., Jin, D., & Ren, B. W. (2024) · Nature Communications · doi:10.1038/s41467-024-44864-5 · Cited by 43 · see citing works

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method

Zou, Z., Wang, Q., Wu, Q., Li, M., Zhen, J., Yuan, D., Zhou, M., Xu, C., Wang, Y., Zhao, Y., et al. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120503 · Cited by 41 · see citing works

Bringing Vibrational Imaging to Chemical Biology with Molecular Probes

Du, J., Wang, H., & Wei, L. (2022) · ACS Chemical Biology · doi:10.1021/acschembio.2c00200 · Cited by 41 · see citing works

Predicting maternal risk level using machine learning models

Mashrafi, S. S. A., Tafakori, L., & Abdollahian, M. (2024) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-07030-9 · Cited by 40 · see citing works

Artifacts of different dimension reduction methods on hybrid CNN feature hierarchy for Hyperspectral Image Classification

Ahmad, M. I., Shabbir, S., Raza, R. A., Mazzara, M., Distefano, S., & Khan, A. (2021) · Optik · doi:10.1016/j.ijleo.2021.167757 · Cited by 40 · see citing works

Prior Image Guided Snapshot Compressive Spectral Imaging

Chen, Y., Wang, Y., & Zhang, H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3265749 · Cited by 39 · see citing works

Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning

Sun, D., Zhou, C., Hu, J., Li, L., & Ye, H. (2022) · Food Chemistry · doi:10.1016/j.foodchem.2022.135166 · Cited by 35 · see citing works

Unsupervised hyperspectral stimulated Raman microscopy image enhancement: denoising and segmentation via one-shot deep learning

Abdolghader, P., Ridsdale, A., Grammatikopoulos, T., Resch, G., Légaré, F., Stolow, A., Pegoraro, A. F., & Tamblyn, I. (2021) · Optics Express · doi:10.1364/oe.439662 · Cited by 34 · see citing works

Machine learning empowered coherent Raman imaging and analysis for biomedical applications

Zhou, Y., Tang, X., Zhang, D., & Lee, H. J. (2025) · Communications Engineering · doi:10.1038/s44172-025-00345-1 · Cited by 31 · see citing works

Nonlinear RNN with noise-immune: A robust and learning-free method for hyperspectral image target detection

Xiao, X., Jiang, C., Jin, L., Huang, H., & Wang, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120490 · Cited by 32 · see citing works

Fast segmentation and multiplexing imaging of organelles in live cells

Zhanghao, K., Li, M., Chen, X., Liu, W., Li, T., Wang, Y., Su, F., Wu, Z., Shan, C., Wu, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57877-5 · Cited by 30 · see citing works

An Improved Hunter-Prey Optimizer-Based DenseNet Model for Classification of Hyper-Spectral Images

Thirumalraj, A., Asha, V., & Kavin, B. P. (2023) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-0876-9.ch005 · Cited by 27 · see citing works

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Li, N., Wang, Z., & Cheikh, F. A. (2024) · Sensors · doi:10.3390/s24102987 · Cited by 28 · see citing works

Advances in Super-resolution Stimulated Raman Scattering Microscopy

Tipping, W. J., Faulds, K., & Graham, D. (2024) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.4c00057 · Cited by 26 · see citing works

Excitation-encoded single-emission shortwave infrared lanthanide fluorophore palette for real-time in vivo multispectral imaging

Zhang, L., Cheng, R., He, Z., Mei, M., Wu, B., Tan, W., Yan, B., Wang, S., & Zhang, F. (2025) · Nature Photonics · doi:10.1038/s41566-025-01736-8 · Cited by 25 · see citing works

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Ji, Y., Park, S. M., Kwon, S., Leem, J. W., Nair, V. V., Tong, Y., & Kim, Y. L. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad111 · Cited by 24 · see citing works

R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution

Liu, J., Zhang, H., Tian, J.-H., Su, Y., Chen, Y., & Wang, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3412033 · Cited by 23 · see citing works

Live-cell fluorescence spectral imaging as a data science challenge

Acuña-Rodriguez, J. P., Mena-Vega, J. P., & Argüello‐Miranda, O. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-00941-x · Cited by 23 · see citing works

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data

Guo, L., Dong, J., Xu, X., Wu, Z., Zhang, Y., Wang, Y., Li, P., Tang, Z., Zhao, C., & Cai, Z. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c04045 · Cited by 22 · see citing works

Coherent Raman scattering microscopy of lipid droplets in cells and tissues

Zhang, C. (2023) · Journal of Raman Spectroscopy · doi:10.1002/jrs.6540 · Cited by 22 · see citing works

Unveiling the frontiers of deep learning: Innovations shaping diverse domains

Ahmed, S. F., Alam, M. S. B., Kabir, M., Afrin, S., Rafa, S. J., Mehjabin, A., & Gandomi, A. H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06259-x · Cited by 26 · see citing works

Perovskite single-detector visible-light spectrometer

Wang, J., Hao, X., Pan, B., Huang, X., Sun, H., & Pei, P. (2022) · Optics Letters · doi:10.1364/ol.478629 · Cited by 21 · see citing works

Photoswitchable vibrational nanoscopy with sub-100-nm optical resolution

Ao, J., Fang, X., Ma, L., Liu, Z., Wu, S., Wu, C., & Ji, M. (2023) · Advanced Photonics · doi:10.1117/1.ap.5.6.066001 · Cited by 21 · see citing works

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

Kassani, P. H., Lu, F., Guen, Y. L., Belloy, M. E., & He, Z. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00525-0 · Cited by 20 · see citing works

High-content stimulated Raman histology of human breast cancer

Ni, H., Dessai, C. V. P., Lin, H., Wang, W., Chen, S., Yuan, Y., Ge, X., Ao, J., Vild, N., & Cheng, J. (2024) · Theranostics · doi:10.7150/thno.90336 · Cited by 19 · see citing works

Deep learning in light–matter interactions

Midtvedt, D., Mylnikov, V., Stilgoe, A. B., Käll, M., Rubinsztein-Dunlop, H. H., & Volpe, G. (2022) · Nanophotonics · doi:10.1515/nanoph-2022-0197 · Cited by 19 · see citing works

Stain-Free Histopathology with Stimulated Raman Scattering Microscopy

Ma, L., Luo, K., Liu, Z., & Ji, M. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c02061 · Cited by 19 · see citing works

Label-Free Cytometric Evaluation of Mitosis via Stimulated Raman Scattering Microscopy and Spectral Phasor Analysis

Hislop, E. W., Tipping, W. J., Faulds, K., & Graham, D. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c00212 · Cited by 18 · see citing works

Heterogeneous Cuckoo Search-Based Unsupervised Band Selection for Hyperspectral Image Classification

Wu, M., Ou, X., Lu, Y., Li, W., Yu, D., Liu, Z., & Ji, C. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3339828 · Cited by 18 · see citing works

AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging

Jiang, Y. Y., Sha, H., Liu, S., Qin, P., & Zhang, Y. (2023) · Biomedical Optics Express · doi:10.1364/boe.498421 · Cited by 16 · see citing works

Simultaneous Dual-Band Hyperspectral Stimulated Raman Scattering Microscopy with Femtosecond Optical Parametric Oscillators

Xu, F. X., Rathbone, E. G., & Fu, D. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c09105 · Cited by 16 · see citing works

Weakly Supervised Identification and Localization of Drug Fingerprints Based on Label-Free Hyperspectral CARS Microscopy

Shi, J., Bera, K., Mukherjee, P., Alex, A., Chaney, E. J., Spencer‐Dene, B., Majer, J., Marjanović, M., Spillman, D. R., Hood, S. R., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c00979 · Cited by 15 · see citing works

Fast denoising and lossless spectrum extraction in stimulated Raman scattering microscopy

Li, Y., Shen, B., Zou, G., Wang, S., Qu, J., Hu, R., & Liu, L. (2021) · Journal of Biophotonics · doi:10.1002/jbio.202100080 · Cited by 15 · see citing works

Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines

You, Y., Zhang, L., Yu, Z., Zhao, D., Bai, X., & Zhang, W. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1620154 · Cited by 14 · see citing works

Self-supervised elimination of non-independent noise in hyperspectral imaging

Ding, G., Liu, C., Yin, J., Teng, X., Tan, Y., He, H., Lin, H., Tian, L., & Cheng, J. (2025) · Newton · doi:10.1016/j.newton.2025.100195 · Cited by 14 · see citing works

Deep learning-based digital volume correlation

Duan, X., & Huang, J. (2022) · Extreme Mechanics Letters · doi:10.1016/j.eml.2022.101710 · Cited by 14 · see citing works

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Yang, K., Wu, F., Guo, H., Chen, D., Deng, Y., Huang, Z., Han, C., Chen, Z., Xiao, R., & Chen, P. (2024) · Land · doi:10.3390/land13111810 · Cited by 14 · see citing works

Visualization of a Limonene Synthesis Metabolon Inside Living Bacteria by Hyperspectral SRS Microscopy

Zhang, J., Shin, J., Tague, N., Lin, H., Zhang, M., Ge, X., Wong, W. W., Dunlop, M. J., & Cheng, J. (2022) · Advanced Science · doi:10.1002/advs.202203887 · Cited by 14 · see citing works

Spatial–spectral sparse deep learning combined with a freeform lens enables extreme depth-of-field hyperspectral imaging

Pan, Y., Niu, Z., Wan, S., Li, X., Cao, Z., Lu, Y., Shao, J., & Wei, C. (2025) · Photonics Research · doi:10.1364/prj.541560 · Cited by 13 · see citing works

Improving prediction of maternal health risks using PCA features and TreeNet model

Jamel, L., Umer, M., Saidani, O., Alabduallah, B. I., Alsubai, S., Ishmanov, F., Kim, T.-H., & Ashraf, I. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1982 · Cited by 12 · see citing works

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D–3D deep learning framework for colorectal cancer detection

Oswald, W., Browning, C. M., Yasmin, R., Deal, J., Rich, T. C., Leavesley, S. J., & Gong, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-64917-5 · Cited by 12 · see citing works

Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach

Bali, A., Wolter, S., Pelzel, D., Weyer, U., Azevedo, T., Lió, P., Kouka, M., Geißler, K., Bitter, T., Ernst, G., et al. (2025) · Cancers · doi:10.3390/cancers17101617 · Cited by 12 · see citing works

In vivo simultaneous nonlinear absorption Raman and fluorescence (SNARF) imaging of mouse brain cortical structures

Francis, A. T., Manifold, B., Carlson, E. C., Hu, R., Hill, A. H., Men, S., & Fu, D. (2022) · Communications Biology · doi:10.1038/s42003-022-03166-6 · Cited by 11 · see citing works

Quantitative Chemical Imaging of Bone Tissue for Intraoperative and Diagnostic Applications

Shin, K. S., Men, S., Wong, A., Cobb-Bruno, C., Chen, E. Y., & Fu, D. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c04354 · Cited by 11 · see citing works

Deep learning assisted classification of spectral photoacoustic imaging of carotid plaques

Cano, C., Rad, N. M., Gholampour, A., van Sambeek, M., Pluim, J. P. W., Lopata, R. G. P., & Wu, M. (2023) · Photoacoustics · doi:10.1016/j.pacs.2023.100544 · Cited by 11 · see citing works

ADMM-DSP: A Deep Spectral Image Prior for Snapshot Spectral Image Demosaicing

Chen, Y., Zhang, H., Wang, Y., Yin, A., & Zhao, B. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3329674 · Cited by 11 · see citing works

Adaptive Center-Focused Hybrid Attention Network for Change Detection in Hyperspectral Images

Jiang, F., Zhang, S., Zhang, M., Gong, M., Zhou, Y., Zhao, W. Z., & Guan, Z. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3534881 · Cited by 11 · see citing works

Unleashing the full potential of hyperspectral imaging: Decoupled image and frequency-domain spatial–spectral framework

He, S., Tian, J., Hao, L., Zhang, S., & Tian, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122870 · Cited by 10 · see citing works

Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data

Liu, X., Duan, C., Cai, W., & Shao, X. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c02720 · Cited by 10 · see citing works

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders

Bench, C. A., Nallala, J., Wang, C., Sheridan, H., & Stone, N. (2022) · Biomedical Optics Express · doi:10.1364/boe.476233 · Cited by 10 · see citing works

2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

Smeesters, L., Venturini, F., Paulus, S., Mahlein, A., Perpetuini, D., Cardone, D., Merla, A., Gac, B., Perrin, S., Trégoat, D., et al. (2025) · Journal of Physics Photonics · doi:10.1088/2515-7647/adbea9 · Cited by 10 · see citing works

Band Engineering and Structural-Geometrical Engineering in 2D/3D van der Waals Heterostructures for Advanced Photodetection and Intelligent Sensing

Yang, M., Gong, K., Cui, Y., Liu, S., Li, G., & Lin, S. (2026) · Nano-Micro Letters · doi:10.1007/s40820-026-02129-4 · Cited by 9 · see citing works

Bolstering Performance Evaluation of Image Segmentation Models With Efficacy Metrics in the Absence of a Gold Standard

Tang, L., Shao, J., Pang, S., Wang, Y., Maxwell, A. E., Hu, X., Gao, Z. Y., Lan, T., & Shao, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3446950 · Cited by 9 · see citing works

LPCN: Lightweight Precise Classification Network for Hyperspectral Remote Sensing Imagery Based on Multiobjective Optimization

Wan, Y., & Zhong, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3403648 · Cited by 8 · see citing works

Hyperspectral Imaging Techniques for Lyophilization: Advances in Data‐Driven Modeling Strategies and Applications

Yu, H., Srisuma, P., Devos, C., Wang, J., Myerson, A. S., & Braatz, R. D. (2025) · Advanced Science · doi:10.1002/advs.202508506 · Cited by 8 · see citing works

The power of fusion: LiDAR meets hyperspectral imaging in a new era of exploration

Kazanskiy, N. L., Doskolovich, L. L., Владимирович, Г. Н., & Khonina, S. N. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.114080 · Cited by 10 · see citing works

Advances in Hyperspectral Imaging for Spoilage Detection in Meat and Poultry: A Non-Invasive Approach

Sherin, Z., Bashir, O., Mondal, I. H., & Pawase, P. A. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105558 · Cited by 8 · see citing works

RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction

Zhou, D., Gu, C., Xu, J., Liu, F., Wang, Q., Chen, G., & Heng, P. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00323 · Cited by 8 · see citing works

Enhancing hyperspectral imaging

Bhargava, R., & Falahkheirkhah, K. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00336-9 · Cited by 8 · see citing works

Automatic quantitative analysis of metabolism inactivation concentration in single bacterium using stimulated Raman scattering microscopy with deep learning image segmentation

Sun, B., Wang, Z., Lin, J., Chen, C., Zheng, G., Yue, S., Wang, H., Kang, X., Chen, X., Hong, W., et al. (2022) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2022.100114 · Cited by 8 · see citing works

An extreme learning machine model based on adaptive multi-fusion chaotic sparrow search algorithm for regression and classification

Zhang, R., Su, J., & Feng, J. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00852-0 · Cited by 8 · see citing works

Viewing 3D spatial biology with highly-multiplexed Raman imaging: from spectroscopy to biotechnology

Li, Y., Sun, Y., & Shi, L. (2024) · Chemical Communications · doi:10.1039/d4cc02319f · Cited by 7 · see citing works

A Nonconvex Activated Fuzzy RNN with Noise-Immune for Time-Varying Quadratic Programming Problems: Application to Plant Leaf Disease Identification

Hu, Y., Du, Q., Luo, J., Yu, C., Zhao, B., & Sun, Y. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010127 · Cited by 7 · see citing works

Binary Neural Network Based on a Programmable Graphene/Si Schottky Diode for In-Sensor Processing Image Sensors

Chen, P., Sun, H., Ming, Z., Tian, Y., & Zhang, Z. (2025) · ACS Nano · doi:10.1021/acsnano.5c04778 · Cited by 7 · see citing works

Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB)

García-Peraza-Herrera, L. C., Horgan, C. C., Sebastien Ourselin, A., Ebner, M., & Vercauteren, T. K. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2160377 · Cited by 7 · see citing works

A Real-Time FPGA Implementation of the LCMV Algorithm for Target Classification in Hyperspectral Images Using LDL Decomposition

Palacios, P., Báscones, D., González, C., & Mozos, D. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3434423 · Cited by 7 · see citing works

Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells

Wei, Z., Liu, W., Yu, W., Liu, X., Yan, R., Liu, Q., & Guo, Q. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231810827 · Cited by 7 · see citing works

Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging

Wei, Z., Liu, X., Yan, R., Sun, G., Yu, W., Liu, Q., & Guo, Q. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1002327 · Cited by 7 · see citing works

Advances in hyperspectral imaging-based non-destructive assessment of tomato quality attributes

Zhang, J., Zhang, Q., Tan, Y., Wang, M., Zhang, X., Li, H., Zhao, R., & Yu, J. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101534 · Cited by 6 · see citing works

AI Technologies in Agriculture: Transforming Farming with Intelligent Solutions

Verma, K. K., & Kumar, A. (2025) · Springer eBooks · doi:10.1007/978-981-95-1268-3_8 · Cited by 6 · see citing works

Methodologies for Assessing Chemical Toxicity to Aquatic Microorganisms: A Comparative Review

Chen, H., Li, Y., Quanzhan, C., Chen, C., & 胡耀娟 (2026) · Molecules · doi:10.3390/molecules31030485 · Cited by 6 · see citing works

Hyperspectral Target Identification Using Physics-Guided Neural Networks with Explainability and Feature Attribution

Klein, N., Carr, A., Hampel-Arias, Z., Zastrow, A., Ziemann, A., & FLYNN, E. B. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10283350 · Cited by 6 · see citing works

Unsupervised Range-Nullspace Learning Prior for Multispectral Images Reconstruction

Chen, Y., Wang, Y., & Zhang, H. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3560430 · Cited by 5 · see citing works

Non-destructive method for assessing fruit quality using modified depthwise separable convolutions on hyperspectral images

Tyagi, D. T., Duraisamy, P., & Sandan, T. (2024) · SPIE Conference on Sensing for Agriculture and Food Quality and Safety · doi:10.1117/12.3015521 · Cited by 5 · see citing works

From Snapshot to Movie: Visualizing Native Silk Spinning Defines the Paradigm for Bio‐Inspired Spinning

Lin, Q., Liu, Y., Li, Y., Li, L., Vollrath, F. W., Ji, M., & Shao, Z. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202511413 · Cited by 4 · see citing works

Methods for Rapid Evaluation of Microbial Antibiotics Resistance

Yabbarov, N. G., Nikolskaya, E. D., Bibikov, S. B., Maltsev, A. A., Chirkina, M., Mollaeva, M. R., Sokol, M. B., Epova, E. Y., Aliev, R. O., & Kurochkin, I. N. (2025) · Biochemistry (Moscow) · doi:10.1134/s0006297924603678 · Cited by 4 · see citing works

Hyperspectral Information Extraction With Full Resolution From Arbitrary Photographs

Kwon, S., Park, S. M., Ji, Y., Sakthivel, H., Leem, J. W., & Kim, Y. L. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3597038 · Cited by 4 · see citing works

Classification of hyperspectral imagery using spectrally partitioned HyperUnet

Paul, A., & Bhoumik, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06532-3 · Cited by 23 · see citing works

Deep learning for analysis of hyperspectral stimulated Raman scattering images

Manifold, B., & Fu, D. (2022) · Advanced Chemical Microscopy for Life Science and Translational Medicine · doi:10.1117/12.2609800

A U-Net Based Approach for High-Accuracy Land Use Land Cover Classification in Hyperspectral Remote Sensing

Khan, A., Patil, C. H., Vibhute, A. D., & Mali, S. M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-53728-8_8 · Cited by 10 · see citing works

CSMUnet: center-sampled selective state space U-Net for hyperspectral image classification

Cui, X., & Zhang, L. (2025) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.19.026511

Multiscale nested U-Net for small sample classification of hyperspectral images

Liu, B., Yu, A., Gao, K., Wang, Y., Yu, X., & Zhang, P. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.016506 · Cited by 12 · see citing works