Abstract: A method of providing a machine learning model (MLM) for use in a process which uses a ML algorithm includes receiving image sensor data and performing processing modules. A first module includes receiving the sensor data, determining a source, parsing source information, converting the sensor data into data items, associating them with meta data information, and ingesting them with associated meta data information. The second module includes accessing a data warehouse and identifying an image keyframe per data item, and associating information indicative of the image keyframe with the respective data item. The third module includes presenting the image keyframe and associated information of the respective data item via an HMI for annotation, receiving an annotation and augmenting the image keyframe. The fourth module includes generating and/or updating a MLM using an annotated dataset, and the fifth module includes uploading the MLM as a task-specific MLM to a storage device.

Abstract: A separator plate, a method for producing the separator plate and its use.

Abstract: Embodiments of the present disclosure provides improved methods for determining the presence of abnormalities in exfoliated cells. In one embodiment, the present disclosure provides methods for reconstructing cellular spectrum of a cell sample by creating a spectral map of the cellular sample, generating a binary mask of the spectral map, removing edge artifacts from each cell, and co-adding spectral data of each pixel corresponding to the cell to reconstruct the spectrum of each cell.

Abstract: Embodiments of the present disclosure provides improved methods for determining the presence of abnormalities in exfoliated cells. In one embodiment, the present disclosure provides methods for reconstructing cellular spectrum of a cell sample by creating a spectral map of the cellular sample, generating a binary mask of the spectral map, removing edge artifacts from each cell, and co-adding spectral data of each pixel corresponding to the cell to reconstruct the spectrum of each cell.

Abstract: Embodiments of the present disclosure provides improved methods for determining the presence of abnormalities in exfoliated cells. In one embodiment, the present disclosure provides methods for reconstructing cellular spectrum of a cell sample by creating a spectral map of the cellular sample, generating a binary mask of the spectral map, removing edge artifacts from each cell, and co-adding spectral data of each pixel corresponding to the cell to reconstruct the spectrum of each cell.

Abstract: Embodiments of the present disclosure provides improved methods for determining the presence of abnormalities in exfoliated cells. In one embodiment, the present disclosure provides methods for reconstructing cellular spectrum of a cell sample by creating a spectral map of the cellular sample, generating a binary mask of the spectral map, removing edge artifacts from each cell, and co-adding spectral data of each pixel corresponding to the cell to reconstruct the spectrum of each cell.

Abstract: Embodiments of the present disclosure provides improved methods for determining the presence of abnormalities in exfoliated cells. In one embodiment, the present disclosure provides methods for reconstructing cellular spectrum of a cell sample by creating a spectral map of the cellular sample, generating a binary mask of the spectral map, removing edge artifacts from each cell, and co-adding spectral data of each pixel corresponding to the cell to reconstruct the spectrum of each cell.