scrutiny
发表于 2025-3-23 11:03:41
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庄严
发表于 2025-3-23 15:10:32
Uwe Westphalms are based on the assumption that the nuclei center should have larger responses than their surroundings in the probability map of the pathological image, which in turn transforms the detection or localization problem into finding the local maxima on the probability map. However, all the existing
锡箔纸
发表于 2025-3-23 21:25:05
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STRIA
发表于 2025-3-24 01:41:14
fect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based a
creatine-kinase
发表于 2025-3-24 04:38:55
Uwe Westphalst CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Fea
Nostalgia
发表于 2025-3-24 06:38:29
Uwe Westphaltical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesi
judicial
发表于 2025-3-24 12:37:15
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凶猛
发表于 2025-3-24 17:33:51
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Condense
发表于 2025-3-24 22:10:44
Uwe Westphalssible without special computationally intensive methods.CliMachine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scali
babble
发表于 2025-3-25 01:52:11
ssible without special computationally intensive methods.CliMachine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scali