chandel
发表于 2025-3-25 07:14:05
Excited Nuclear States for Li-13 (Lithium),hat it achieves state of the art accuracy while being extremely fast. The experimental results are also demonstrated using AIndra dataset collected by us, which also captures the inter observer variability.
nostrum
发表于 2025-3-25 08:45:05
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Optometrist
发表于 2025-3-25 13:39:38
Evaluating Out-of-the-Box Methods for the Classification of Hematopoietic Cells in Images of Stainede challenging dataset and we show that while generic classical machine learning approaches cannot compete with specialized algorithms, even out-of-the-box deep learning methods already yield valuable results. Our findings indicate that automated analysis of bone marrow images becomes possible with the advent of convolutional neural networks.
Esalate
发表于 2025-3-25 18:46:09
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stressors
发表于 2025-3-25 23:37:14
Excited Nuclear States for Li-11 (Lithium),nds to the selected voxel. We trained the generators by using an MRI image and a 3D pathology image, the latter was first reconstructed from a spatial series of the 2D pathology images and was then registered to the MRI image.
排他
发表于 2025-3-26 01:59:39
https://doi.org/10.1007/978-3-662-47801-1especially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.
冷峻
发表于 2025-3-26 06:24:27
Excited Nuclear States for Li-7 (Lithium),ches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.
GRE
发表于 2025-3-26 10:40:35
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Feigned
发表于 2025-3-26 16:13:12
Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learningespecially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.
外形
发表于 2025-3-26 20:23:33
Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Contentches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.