Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided d
The optimization algorithm and loss function were stochastic gradient descent and the cross entropy loss function, respectively. Twenty epochs were used, and the initial learning rate was set to 0.01. The momentum was 0.9 and the batch size for training was 100. Results and discussion Cross-data...
By doing this, COSAM promotes feature interactions between multiple frames in an interpretable and computationally effective way in terms of the number of parameters and FLOPs (refer Section 5.1.4 for more information). 2.2. Object co-segmentation approaches Depending on the underlying algorithm, co...
An algorithm of fault parameter determination using distribution of small earthquakes and parameters of regional stress field and its application to Tangsh... It is known that clustered small earthquakes often occur in the fault plane vicinities of large earthquakes.Based on the simulated annealing an...
We consider as well the technical implications of the algorithm implementation. In order to reduce runtime which is incurred by the vast theoretical search space, experiments on the MareNostrum Supercomputer were made to understand the significance of the different search space dimensions on the ...
It gives an algorithm for addition, subtraction, multiplication, division and square root, and requires that implementations produce the same result as that algorithm. Thus, when a program is moved from one machine to another, the results of the basic operations will be the same in every bit ...
Data preparation and aggregation are needed to build the right model, but they are repetitive, time-consuming tasks that depend on locating appropriate data quality sources. Likewise, hyperparameter tuning can take a lot of time to iterate to get the right algorithm performance. It involves a tri...
In grayscaling, the value of each pixel in an image is calculated from the values of its red, green, and blue channels by a specific algorithm to obtain a gray value that represents the luminance of that pixel [25]. The purpose of grayscaling is to simplify image processing and reduce ...
However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological ...
Several attempts have been made to overcome the problem of class imbalance by using different approaches and techniques. These techniques can be grouped into data-level approaches, algorithm level methods and hybrid techniques. While data level approaches modify the distribution of training set to resto...