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Titlebook: Non-Linear Filters for Mammogram Enhancement; A Robust Computer-ai Vikrant Bhateja,Mukul Misra,Shabana Urooj Book 2020 Springer Nature Sing

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41#
發(fā)表于 2025-3-28 18:39:04 | 只看該作者
42#
發(fā)表于 2025-3-28 19:23:10 | 只看該作者
Non-Linear Polynomial Filters for Edge Enhancement of Mammogramsever, NPF ‘Prototypes: . and .′ have been performance limited; as the contrast improvement has been accompanied with a severe background suppression in mammograms. This affected the visualization of other anatomical structures and diagnostic features in the vicinity of the ROI; these features equall
43#
發(fā)表于 2025-3-29 02:15:47 | 只看該作者
Book 2020 a valuable asset for radiologists and medical experts (oncologists), as mammogram visualization can enhance the precision of their diagnostic analyses; and for researchers and engineers, as the analysis of non-linear filters is one ofthe most challenging research domains in image processing..?.?. .?.
44#
發(fā)表于 2025-3-29 06:57:49 | 只看該作者
45#
發(fā)表于 2025-3-29 07:50:32 | 只看該作者
d propagation of the values in an attack-tree. It uses an attack graph to model the attack paths and applies probability distributions for the input values to consider the uncertainty of predictions and expert judgments. We also describe experiences and lessons-learned for conducting an expert elici
46#
發(fā)表于 2025-3-29 14:31:50 | 只看該作者
47#
發(fā)表于 2025-3-29 17:47:08 | 只看該作者
48#
發(fā)表于 2025-3-29 20:50:57 | 只看該作者
Vikrant Bhateja,Mukul Misra,Shabana Uroojlow-enough communication latencies, adequately accurate localization plus update rates equal to the task of dependably controlling mobile cars and robots at decimeter level. With respect to engineering methods, we argue the strong need for approaches, which will ensure the dependability of learning
49#
發(fā)表于 2025-3-30 02:59:43 | 只看該作者
50#
發(fā)表于 2025-3-30 07:49:13 | 只看該作者
Vikrant Bhateja,Mukul Misra,Shabana Uroojlow-enough communication latencies, adequately accurate localization plus update rates equal to the task of dependably controlling mobile cars and robots at decimeter level. With respect to engineering methods, we argue the strong need for approaches, which will ensure the dependability of learning
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