Abstract: Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.

Abstract: Systems and methods for personalized cancer therapy using analysis of pathology slides to target regions in a single sample that interrogates the feature data of a relatively large number of cells. The disclosure describes pathology case review tools of the future which include analysis, visualization and prediction modeling to provide novel information to the pathologist for the diagnosis of disease. This disclosure further describes a user interface to assist the physicians that make that diagnosis, pathologists. Complex computer learning algorithms will combine and mine these data sets to recommend optimal treatment strategies. A computer interface is provided which allows a pathologist to access those data instantly to make a more informed and accurate diagnosis.

Abstract: Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.

Abstract: Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.

Abstract: Systems and methods for personalized cancer therapy using analysis of pathology slides to target regions in a single sample that interrogates the feature data of a relatively large number of cells. The disclosure describes pathology case review tools of the future which include analysis, visualization and prediction modeling to provide novel information to the pathologist for the diagnosis of disease. This disclosure further describes a user interface to assist the physicians that make that diagnosis, pathologists. Complex computer learning algorithms will combine and mine these data sets to recommend optimal treatment strategies. A computer interface is provided which allows a pathologist to access those data instantly to make a more informed and accurate diagnosis.

Abstract: Systems and methods for personalized cancer therapy using analysis of pathology slides to target regions in a single sample that interrogates the feature data of a relatively large number of cells. The disclosure describes pathology case review tools of the future which include analysis, visualization and prediction modeling to provide novel information to the pathologist for the diagnosis of disease. This disclosure further describes a user interface to assist the physicians that make that diagnosis, pathologists. Complex computer learning algorithms will combine and mine these data sets to recommend optimal treatment strategies. A computer interface is provided which allows a pathologist to access those data instantly to make a more informed and accurate diagnosis.

Abstract: Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.

Abstract: Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.

Abstract: Systems and methods for personalized cancer therapy using analysis of pathology slides to target regions in a single sample that interrogates the feature data of a relatively large number of cells. The disclosure describes pathology case review tools of the future which include analysis, visualization and prediction modeling to provide novel information to the pathologist for the diagnosis of disease. This disclosure further describes a user interface to assist the physicians that make that diagnosis, pathologists. Complex computer learning algorithms will combine and mine these data sets to recommend optimal treatment strategies. A computer interface is provided which allows a pathologist to access those data instantly to make a more informed and accurate diagnosis.

Abstract: Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.