Let us suppose that we have t p — number of correctly classified targets, t n — number of correctly classified non-targets, f p — number of misclassified non-targets, f n — number of misclassified targets.
Hematoxylin and eosin (H&E) stained, FFPE slides were used to guide microdissections of histologically classified targets from unstained, serial FFPE slides.
However, as we use multiple-instance learning while classifying target from its background, it has the best recall in both of the two dataset.
However, as we use multiple-instance learning while classifying target from its background, it has the best precision in both of the two dataset.
However, by enhancing the training with supplementary labeled data from a related source domain, the model's ability to classify target instances can be improved.
After classifying target groups into the above categories, we visually presented target groups for each vaccine and compared patterns for different vaccines and between countries.
To better understand the biological functions of these genes in soybean, GO analysis [ 29] was employed to classify target genes based on their involved biological processes.
The development of prediction models using the TNM staging system, primary tumor (T), regional lymph nodes (N), and distant metastasis (M), with using basic clinical covariates to classify target patients as high-risk or low-risk for treatment recommendation has been used for more than a decade.
In Section 2.3, we introduce an alpha beta filter classifier, which classifies targets as either stationary or moving.
This paper presents a novel method to detect and classify targets obscured by foliage based on real data collected by a bistatic ultra-wideband (UWB) radar system.
