Abstract: A communications system may include a first communications device having a first memory storing a crypto-variable (CV) array having N number of crypto-variables (CVs). A first processor cooperates with the first memory and selects M number of CVs from the CV array, the M number having identifiers associated therewith, generates a key using a cryptographic operation and based upon the selected M number of CVs, generates an encrypted message using the key, and transmits the identifiers of the selected M number of CVs and the encrypted message. A second communications device includes a second memory and the CV array stored therein. A second processor cooperates with the second memory and receives the identifiers for the selected M number of CVs and the encrypted message, generates the key using the cryptographic operation based upon the M number of CVs, and decrypts the encrypted message based upon the key.

Abstract: Embodiments described herein provide a platform, device and process for digital pathology that enable multi-level annotation and visualization of histopathologic slides using a modular arrangement of deep convolutional neural networks (CNNs). The CNNs can be trained using pathology images (e.g., in some cases increasing the base of data by breaking larger fields of view into smaller ones) to learn features consistent with certain pathologies. The platform can use the CNNs to visually annotate pathology slides at an interface tool of a display device. The platform can automate the process of selection, as well as provide an opportunity for the pathologist to see a depiction of predicted results. The platform can use the CNNs to identify regions of interest on pathology slides. The interface tool can enable a predicted region of interest (ROI) type to be visually presented on a surface map showing the basis of the prediction.

Abstract: Embodiments described herein provide a platform, device and process for digital pathology that enable multi-level annotation and visualization of histopathologic slides using a modular arrangement of deep convolutional neural networks (CNNs). The CNNs can be trained using pathology images (e.g., in some cases increasing the base of data by breaking larger fields of view into smaller ones) to learn features consistent with certain pathologies. The platform can use the CNNs to visually annotate pathology slides at an interface tool of a display device. The platform can automate the process of selection, as well as provide an opportunity for the pathologist to see a depiction of predicted results. The platform can use the CNNs to identify regions of interest on pathology slides. The interface tool can enable a predicted region of interest (ROI) type to be visually presented on a surface map showing the basis of the prediction.