To remove noisy voxels such as isolated tiny regions during 3-D process (blue R1 in slice S5), we made a simple connected component selection as following: 1) when the noise arises in the starting slice, we select the isolated region that is closest to the bounding box center. 2) when...
as depicted in Fig.1. While a typical convolutional neural network is geared towards image classification, taking an image as input and producing a singular label as output, the demands of biomedical contexts entail not only detecting the presence...
The concept of the neurovascular unit as the key brain component affected by stroke is controversial, because current definitions of this entity neglect mechanisms that control perfusion and reperfusion of arteries and arterioles upstream of the cerebral microcirculation. Indeed, although definitions vary,...
The generator network receives 100-dimensional seed vectors of real-valued numbers in range [-1, 1] as input, and during training we uniformly randomly sample each seed vector component independently. The output of the generator is a batch of nucleotide log probability matrices (each 205 nt long...
This minimal example suggests that we can benefit from a single hidden layer in a neural network. In fact, there are some proven upper bound of the number of neurons for different target functions. Boolean function f:\{0,1\}^d\rightarrow\{0,1\} can be represented by a FNN ( \si...
Small enclosure size prevents elephants from moving long distances and freely interacting with a large network of conspecifics; natural foraging is replaced with a limited zoo diet. Exercise is extremely constrained, even though elephants are physically and cognitively adapted for long-distance movement ...
Following the definitions of loss of entrainment in [15], we investigate internal versus external loss of entrainment in the CPG models. Internal loss of entrainment occurs when part of the chain follows [Math Processing Error]ωi∗=ωf but for the rest of the chain [Math Processing Error]...
The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigati
Before the shift to deep neural networks, change detection was driven by Principal Component Analysis [7] (PCA), Decision Trees [8], Change Vector Analysis [9] (CVA), Difference or Ratio methods [10], Markov chains [11], and other methods [12]. Some neural network-based approaches ...
Okay okay, enough definitions. Point is - our line drawing exercise is a very simple example of supervised machine learning: the points are the training set (X is input and Y is output), the line is the approximated function, and we can use the line to find Y values for X values that...