Abstract: Labeling protocols for training datasets and systems and methods for classifying objects of interest and identifying backgrounds. Labeling protocols described herein enable grid units within an image to be excluded from contributing to a loss function, and further enable granular controls for specifying which grid units should contribute to object predictions. A method partitioning an input image into a plurality of grid units and individually processing each of the plurality of grid units with a neural network configured to calculate a confidence score indicating a likelihood that a grid unit comprises an object of interest. The method includes generating a bounding box around the object of interest, identifying one or more grid units of the plurality of grid units that comprise a portion of the bounding box, and identifying which of the one or more grid units comprises a center point of the bounding box.

Abstract: The disclosure relates to machine learning classification of cells/particles in microscopy images. A method includes inputting an image having invisible features into an initial neural network classifier (INNC) of a convolutional neural network. The INNC is trained using images with ground truth derived from out-of-channel mechanisms. The method includes generating an intermediate classification from the original image. The intermediate classification and the original image are input into a final neural network classifier (FNNC) that comprises one or more bypass layers to feed forward an initial, final classification from a final activation layer to a final convolutional layer thereby bypassing a final pooling layer. The final convolutional layer has an increased kernel size and more filters than the initial convolutional layer. The final classification is generated based on the invisible features in the original image and outputted.

Abstract: Systems, methods, and devices for classifying or detecting mold samples or training computer models (such as neural networks), are disclosed. A method includes obtaining a microscopy image of a mold sample. The method includes determining a classification of the mold sample based on non-image data corresponding to the mold sample. The method further includes training a computer model based on the microscopy image and a label indicating the classification.

Abstract: Systems, methods, and devices for training models or algorithms for classifying or detecting particles or materials in microscopy images are disclosed. A method includes receiving a plurality of microscopy images of a specimen and a classification for the specimen. The plurality of microscopy images includes a first image captured at a first magnification and a second image captured at the first magnification with a different focus than the first image. The method includes training a machine learning model or algorithm using the plurality of images, wherein the first image and the second image are provided with one or more labels indicating the classification.

Abstract: Systems, methods, and devices for training models or algorithms for classifying or detecting particles or materials in microscopy images are disclosed. A method includes receiving a plurality of microscopy images of a specimen and a classification for the specimen. The plurality of microscopy images includes a first image captured at a first magnification and a second image captured at the first magnification with a different focus than the first image. The method includes training a machine learning model or algorithm using the plurality of images, wherein the first image and the second image are provided with one or more labels indicating the classification.

Abstract: Systems, methods, and devices for classifying or detecting mold samples or training computer models (such as neural networks), are disclosed. A method includes obtaining a microscopy image of a mold sample. The method includes determining a classification of the mold sample based on non-image data corresponding to the mold sample. The method further includes training a computer model based on the microscopy image and a label indicating the classification.

Abstract: The disclosure relates to machine learning classification of cells/particles in microscopy images. A method includes inputting an image having invisible features into an initial neural network classifier (INNC) of a convolutional neural network. The INNC is trained using images with ground truth derived from out-of-channel mechanisms. The method includes generating an intermediate classification from the original image. The intermediate classification and the original image are input into a final neural network classifier (FNNC) that comprises one or more bypass layers to feed forward an initial, final classification from a final activation layer to a final convolutional layer thereby bypassing a final pooling layer. The final convolutional layer has an increased kernel size and more filters than the initial convolutional layer. The final classification is generated based on the invisible features in the original image and outputted.