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Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference.

, , , , , , and . Comput. Methods Programs Biomed., (2021)

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Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference., , , , , , and . Comput. Methods Programs Biomed., (2021)Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection., , , , and . CoRR, (2021)Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques., , , , and . IDEAL (1), volume 11314 of Lecture Notes in Computer Science, page 642-650. Springer, (2018)Granulometry-Based Descriptor for Pathological Tissue Discrimination in Histopathological Images., , , and . ICIP, page 1413-1417. IEEE, (2018)Proportion constrained weakly supervised histopathology image classification., , , , and . Comput. Biol. Medicine, (2022)Towards the On-Demand Whole Slide Image Generation: Prostate Patch Synthesis Through a Conditional Progressive Growing GAN., , , , , and . EUSIPCO, page 1070-1074. IEEE, (2023)Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor., , , , and . IDEAL (1), volume 11871 of Lecture Notes in Computer Science, page 39-46. Springer, (2019)A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes., , , , , and . Comput. Methods Programs Biomed., (2019)