Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: Systems, methods, devices, and other techniques using machine learning for interpreting, or assisting in the interpretation of, biologic specimens based on digital images are provided. Methods for improving image-based cellular identification, diagnostic methods, methods for evaluating effectiveness of a disease intervention, and visual outputs useful in assisting professionals in the interpretation of biologic specimens are also provided.

Abstract: An apparatus and method are provided for imaging and analyzing images of tissue samples. The apparatus includes an imager, a lighting system, and a processor. The imager is configured to capture images within a selectable field of view. A tissue sample container is positionable within the field of view. The imager is configured to capture images of a plurality of tissue sample containers. The lighting system is configured to illuminate the field of view. The processor is configured to receive a first plurality of captured images of tissue sample containers. The processor is configured to analyze the first plurality of captured images and to determine whether there is tissue missing from any ones of the first plurality of captured images.

Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: An apparatus and method are provided for imaging and analyzing images of tissue samples. The apparatus includes an imager, a lighting system, and a processor. The imager is configured to capture images within a selectable field of view. A tissue sample container is positionable within the field of view. The imager is configured to capture images of a plurality of tissue sample containers. The lighting system is configured to illuminate the field of view. The processor is configured to receive a first plurality of captured images of tissue sample containers. The processor is configured to analyze the first plurality of captured images and to determine whether there is tissue missing from any ones of the first plurality of captured images.

Abstract: Systems, methods, devices, and other techniques using machine learning for interpreting, or assisting in the interpretation of, biologic specimens based on digital images are provided. Methods for improving image-based cellular identification, diagnostic methods, methods for evaluating effectiveness of a disease intervention, and visual outputs useful in assisting professionals in the interpretation of biologic specimens are also provided.