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Beilstein J. Nanotechnol. 2025, 16, 1129–1140, doi:10.3762/bjnano.16.83
Figure 1: Low- and high-resolution fast-AFM scans of two different locations on a Celgard® 2400 membrane surf...
Figure 2: Low- and high-resolution AFM scans of two different locations on a titanium film, as well as the up...
Figure 3: p-Values for six different metrics (PSNR, SSIM, Fourier Sharpness, PI, Ma, and NIQE) for the two da...
Figure 4: AFM expert survey results. Three experts were asked to judge a blind set of samples and to score ea...
Figure 5: Example of an image set given to AFM experts as part of the survey. The dpi was set to 1000 to ensu...
Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66
Figure 1: Linear decision boundary (green straight line) that separates between samples belonging to two diff...
Figure 2: Performance as function of sample data set size for traditional machine learning algorithms and dee...
Figure 3: Upper left: example of a deep neural network (DNN). Upper right: example of a shallow neural networ...
Figure 4: (a) Description of the convolutional layer of a CNN and (b) the convolution operation. In (b) the o...
Figure 5: Loss function as a function of the number of epochs for the training set (blue line) and the testin...
Figure 6: Predictions of the probability for a healthy cytoskeleton made by the model on testing set and trea...
Figure 7: Example of images of healthy (top) and diseased (bottom) Pf-EV exposed cells from the test set and ...
Figure 8: Average intensity for all images in the testing set and the images of treated cytoskeleton for (a) ...
Figure 9: Class activation maps shown below the AFM images for selected images of healthy and Pf-EV-exposed c...
Figure 10: Class activation maps shown below the AFM images for selected images of healthy and Pf-EV-exposed c...