\uD83C\uDDE6 A-D
Name | Definition | Related articles |
|---|---|---|
annotation | the process of carefully drawing a digital outline around an object using annotation tools | |
anchor point | a bending point to modify the outline of an annotation or region of interest | |
background | background is the definition of an area where no sample/specimen is visible | |
confusion matrix | a table chart used to evaluate the performance of a computer vision AI algorithm, where the actual observations are compared to those of the AI model. The matrix offers an overview of correct and incorrect predictions made by the model. The table lists the four values needed for calculating performance statistics: (1) true positives, (2) false positives, (3) true negatives, and (4) false negatives. A well-performing algorithm should have higher values along the main diagonal of the confusion matrix (true-positive to true-negative) and low values on the other positions (false-positive to false-negative). |
\uD83C\uDDEA E-K
Name | Definition | Related articles |
|---|---|---|
false-positives | a detection error where the algorithm falsely registers the presence of an object | |
false-negatives | a detection error where the algorithm falsely registers the absence of an object | |
ground truth | the annotations in the input image data done by the user. The deep learning model is trained to target morphological structures in novel image data, similar to the ground truth annotations. | |
image classification | a computer vision task for categorizing groups of pixels or vectors within a given image |
\uD83C\uDDF1 L-O
Name | Definition | Related articles |
|---|---|---|
label, labeling, label category | a label is a verbal categorization assigned to an object in a particular image upon annotating the object | |
multichannel images | images that consist of different channels taken under different laser light conditions that are stacked one above another | |
multidimensional microscopy images | images visualizing the dynamics of cellular structures in space and through time. These can be three-dimensional (3D), four-dimensional (4D), multichannel or time-series images etc. Multidimensional images are acquired with optical sectioning techniques or laser-scanning microscopy techniques, time-lapse or multifocal-planes recordings. Such images contain additional layers of information on cell movements, structural dynamics, multichannel spectrums and wave-lengths. | |
object detection | a computer vision task for identifying objects within a digital image |
\uD83C\uDDF5 P-S
Name | Definition | Related articles |
|---|---|---|
predicted areas | areas within an image assigned to a particular label | |
predicted pixel label | the label the AI-algorithm assigns a pixel to. This means each pixel in an image is assigned a class label from a predefined set. In other words, the “predicted pixel label“ is the label the AI-algorithm assigned to a specific pixel. | |
random split | the process of letting the platform decide which images are used for training and which ones for validation. | |
RGB images | images that consist of 3 channels: red, green and blue. | |
semantic segmentation | a computer vision task for segmenting an image through assigning a label to each pixel within. |
\uD83C\uDDF9 T-Z
Name | Definition | Related articles |
|---|---|---|
time-series images | a set of images taken from the same specimen and with the same modalities but at different times | |
training images | the images that are used as visual data input to train a computer vision algorithm | |
unweighted computing | a mode of algorithm computation where each visual input or label has equal relative importance within the calculation | |
validation images | the images within a testing dataset used in order to evaluate the performance of a computer vision algorithm | |
WSI | Whole-Slide Imaging (WSI) is the process of acquiring high-resolution digital pathology images through the scanning of conventional glass slides |