SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Quantification of histochemical staining by color deconvolution.

Ruifrok, A. C. C., & Johnston, D. A. (2001) · PubMed · Cited by 2,029 · see citing works

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536 · see citing works

A method for normalizing histology slides for quantitative analysis

Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro · doi:10.1109/isbi.2009.5193250 · Cited by 1,482 · see citing works

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works

Deep learning for magnification independent breast cancer histopathology image classification

Bayramoğlu, N., Kannala, J., & Heikkilä, J. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900002 · Cited by 468 · see citing works

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works

Using filter banks in Convolutional Neural Networks for texture classification

Andrearczyk, V., & Whelan, P. F. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.08.016 · Cited by 274 · see citing works

Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks

Chen, H., Dou, Q., Wang, X., Qin, J., & Heng, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10140 · Cited by 264 · see citing works

Automatic detection and classification of leukocytes using convolutional neural networks

Zhao, J., Zhang, M., Zhou, Z., Chu, J., & Cao, F. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1590-x · Cited by 219 · see citing works

A Complete Color Normalization Approach to Histopathology Images Using Color Cues Computed From Saturation-Weighted Statistics

Li, X., & Plataniotis, K. N. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2405791 · Cited by 137 · see citing works

A Deep Learning Approach for Semantic Segmentation in Histology Tissue Images

Wang, J., MacKenzie, J. D., Ramachandran, R., & Chen, D. Z. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_21 · Cited by 73 · see citing works

Local Binary Pattern for automatic detection of Acute Lymphoblastic Leukemia

Singhal, V., & Singh, P. (2014) · doi:10.1109/ncc.2014.6811261 · Cited by 71 · see citing works

Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis

Xu, J., Xiang, L., Wang, G., Ganesan, S., Feldman, M. D., Shih, N. N. C., Gilmore, H. L., & Madabhushi, A. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.04.002 · Cited by 65 · see citing works

Human Epithelial Type 2 cell classification with convolutional neural networks

Bayramoğlu, N., Kannala, J., & Heikkilä, J. (2015) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2015.7367705 · Cited by 44 · see citing works

Method for diagnosis of acute lymphoblastic leukemia based on vit-cnn ensemble model

Jiang, Z., Dong, Z., Wang, L., & Jiang, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/7529893 · Cited by 124 · see citing works

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Kasani, P. H., Park, S.-W., & Jang, J.-W. (2020) · Diagnostics · doi:10.3390/diagnostics10121064 · Cited by 76 · see citing works

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I

(2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7 · Cited by 23 · see citing works

In situ switching layer-by-layer assembly: one-pot rapid layer assembly via alternation of reductive and oxidative electropolymerization

Li, M., Zhang, J., Nie, H., et al. (2013) · Chemical Communications · doi:10.1039/c3cc43629b · Cited by 37 · see citing works

Preparation of the nucleoside-containing nanolayered film by the layer-by-layer deposition technique

Shimazaki, Y., Mitsuishi, M., Ito, S., et al. (1998) · Thin Solid Films · doi:10.1016/s0040-6090(98)00966-3 · Cited by 13 · see citing works

Optical Switching and Fluorescence Modulation in Photochromic Metal Complexes

Fernandez‐Acebes, A., & Lehn, J. (1998) · Advanced Materials · doi:10.1002/(sici)1521-4095(199812)10:18 · Cited by 145 · see citing works

Thin multifunctional coatings for textiles based on the layer-by-layer application of polyaromatic hybrid nanoparticles

Babaeipour, S., Nousiainen, P., Kimiaei, E., et al. (2024) · Materials Advances · doi:10.1039/d4ma00085d · Cited by 31 · see citing works

Formation of a thin and continuous MOF membrane with 2-D MOF nanosheets as seeds via layer-by-layer growth

Ma, Y., Dong, Z., Meng, Y., et al. (2019) · Chemical Communications · doi:10.1039/c9cc04851k · Cited by 74 · see citing works