Abstract: Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.

Abstract: According to some embodiments, an invoice data store contains electronic records for invoices associated with customers and an enterprise, including an electronic record identifier and a set of attribute values. An electronic invoicing system computer server may receive invoice data (associated with a customer of the enterprise) from remote invoice platforms, and the server may create invoice documents in first and second formats based on information in the invoice data store. It may be determined that the invoice documents represent “work in process” and, as a result, the system may prevent the invoice documents from being transmitted to a customer device. An adjustment to the invoice document in an intermediate format is received and the invoice document in at least one of the first and second format is automatically updated. When it is later determined that the invoice documents no longer represent “work in process,” the system electronically transmits the invoice documents to the customer device.

Abstract: Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.

Abstract: According to some embodiments, an invoice data store contains electronic records for invoices associated with customers and an enterprise, including an electronic record identifier and a set of attribute values. An electronic invoicing system computer server may receive invoice data (associated with a customer of the enterprise) from remote invoice platforms, and the server may create invoice documents in first and second formats based on information in the invoice data store. It may be determined that the invoice documents represent “work in process” and, as a result, the system may prevent the invoice documents from being transmitted to a customer device. An adjustment to the invoice document in an intermediate format is received and the invoice document in at least one of the first and second format is automatically updated. When it is later determined that the invoice documents no longer represent “work in process,” the system electronically transmits the invoice documents to the customer device.