Ultrata, LLC

Kendall Square Research Corporation

Mangosoft Corporation

Sun Microsystems, Inc.

The University of Texas System

University of Houston System

Ruth Shelley

Edward N. Kittlitz

Steven J. Frank

James B. Rothnie

Benson I. Margulies

Glen Dudek

William F. Mann

Steven Frank

Henry III Burkhardt

Steven J Frank

Larry Reback

Henry Burkhardt, Iii

Frederick D Weber

Scott H Davis

John B Carter

Benson I Margulies

Daniel J Dietterich

Nathan Goodman

Karen Martirosyan

James B Rothnie

Linda O Lee

William Frederick Mann, Iii

Robert S Phillips

Linda Q Lee

Hsin H Lee

Paul A Binder

David I Epstein

David G Porter

William Abraham

Dana R Kelly

Stephen W Morss

Amy Liu

John A Roskosz

John Woods

Jinzhong Yang

Thomas G Hansen

Paul Wisdom

Brett D Byers

Ronald Zhu

Edward N Kittlitz

Richard Wu

Peter C Schnorr

Henry Iii Burkhardt

Iii Burkhardt Henry

William F Mann

First and second predictors are used to identify regions of interest in a medical image. The first predictor is configured to maximize precision in identifying one or more abnormalities that it was trained to recognize, or at least to have greater precision than the second predictor. The second predictor is configured or selected to maximize sensitivity (i.e., recall) in identifying one or more abnormalities that it was trained to recognize, or at least to have greater sensitivity than the first predictor. The predictions of the first and second predictors may be used to create a digital map image showing the locations of regions of interest identified by the first and second predictors. Alternatively or in addition, the predictions may be used to rank images or image sets in terms of review priority.

Multistage region-of-interest identification in medical images

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles, which are sifted based on a visual criterion. The visual criterion may be one or more of image entropy, density, background percentage, or other discriminator. A convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

Digital tissue segmentation

Methods and systems for representing a tissue segmentation from a source digital image computationally generate, from a source digital image of an anatomic region, a digital tissue segmentation visually indicating regions of interest corresponding to an abnormal condition associated with at least portions of the anatomic region. The source image and the tissue segmentation may be alternately displayed in registration on a mobile device at a gesturally selected magnification level.

Digital tissue segmentation and viewing

A hardware-based processing node of an object memory fabric can comprise a memory module storing and managing one or more memory objects within an object-based memory space. Each memory object can be created natively within the memory module, accessed using a single memory reference instruction without Input/Output (I/O) instructions, and managed by the memory module at a single memory layer. The memory module can provide an interface layer below an application layer of a software stack. The interface layer can comprise one or more storage managers managing hardware of a processor and controlling portions of the object-based memory space visible to a virtual address space and physical address space of the processor. The storage managers can further provide an interface between the object-based memory space and an operating system executed by the processor and an alternate object memory based storage transparent to software using the interface layer.

Memory fabric software implementation

Embodiments of the present invention are directed to an instruction set of an object memory fabric. This object memory fabric instruction set can be used to define arbitrary, parallel functionality such as: direct object address manipulation and generation without the overhead of complex address translation and software layers to manage differing address space; direct object authentication with no runtime overhead that can be set based on secure 3rd party authentication software; object related memory computing in which, as objects move between nodes, the computing can move with them; and parallelism that is dynamically and transparent based on scale and activity. These instructions are divided into three conceptual classes: memory reference including load, store, and special memory fabric instructions; control flow including fork, join, and branches; and execute including arithmetic and comparison instructions.

Object memory instruction set

Embodiments of the invention provide systems and methods to implement an object memory fabric. Object memory modules may include object storage storing memory objects, memory object meta-data, and a memory module object directory. Each memory object and/or memory object portion may be created natively within the object memory module and may be a managed at a memory layer. The memory module object directory may index all memory objects and/or portions within the object memory module. A hierarchy of object routers may communicatively couple the object memory modules. Each object router may maintain an object cache state for the memory objects and/or portions contained in object memory modules below the object router in the hierarchy. The hierarchy, based on the object cache state, may behave in aggregate as a single object directory communicatively coupled to all object memory modules and to process requests based on the object cache state.

Utilization of a distributed index to provide object memory fabric coherency

Embodiments of the invention provide systems and methods for managing processing, memory, storage, network, and cloud computing to significantly improve the efficiency and performance of processing nodes. More specifically, embodiments of the present invention are directed to an instruction set of an object memory fabric. This object memory fabric instruction set can be used to provide a unique instruction model based on triggers defined in metadata of the memory objects. This model represents a dynamic dataflow method of execution in which processes are performed based on actual dependencies of the memory objects. This provides a high degree of memory and execution parallelism which in turn provides tolerance of variations in access delays between memory objects. In this model, sequences of instructions are executed and managed based on data access. These sequences can be of arbitrary length but short sequences are more efficient and provide greater parallelism.

Object memory data flow instruction execution

Embodiments of the invention provide systems and methods to implement an object memory fabric including hardware-based processing nodes having memory modules storing and managing memory objects created natively within the memory modules and managed by the memory modules at a memory layer, where physical address of memory and storage is managed with the memory objects based on an object address space that is allocated on a per-object basis with an object addressing scheme. Each node may utilize the object addressing scheme to couple to additional nodes to operate as a set of nodes so that all memory objects of the set are accessible based on the object addressing scheme, which defines invariant object addresses for the memory objects that are invariant with respect to physical memory storage locations and storage location changes of the memory objects within the memory module and across all modules interfacing the object memory fabric.

Implementation of an object memory centric cloud

Embodiments of the invention provide systems and methods to implement a hardware-based multi-node processing system of an object memory fabric. Hardware-based processing nodes operatively coupled may each include object memory modules storing and managing memory objects, each memory object being created natively within the memory module and managed by the memory module at a memory layer, and each memory object including memory object data and memory object metadata. The memory object metadata may include triggers that specify additional operations to be executed by any object memory module of the hardware-based processing nodes when the respective memory object is located at the respective object memory module and accessed as part of the respective object memory module processing requests.

Managing meta-data in an object memory fabric

Embodiments of the invention provide systems and methods for managing processing, memory, storage, network, and cloud computing to significantly improve the efficiency and performance of processing nodes. More specifically, embodiments of the present invention are directed to a hardware-based processing node of an object memory fabric. The processing node may include a memory module storing and managing one or more memory objects, the one or more memory objects each include at least a first memory and a second memory, wherein: each memory object is created natively within the memory module, and each memory object is accessed using a single memory reference instruction without Input/Output (I/O) instructions; and a router configured to interface between a processor on the memory module and the one or more memory objects; wherein a set of data is stored within the first memory of the memory module; wherein the memory module dynamically determines that at least a portion of the set of data will be transferred from the first memory to the second memory; and wherein, in response to the determination that at least a portion of the set of data will be transferred from the first memory to the second memory, the router is configured to identify the portion to be transferred and to facilitate execution of the transfer.

Infinite memory fabric hardware implementation with router

Embodiments of the invention provide systems and methods for managing processing, memory, storage, network, and cloud computing to significantly improve the efficiency and performance of processing nodes. More specifically, embodiments of the present invention are directed to an instruction set of an object memory fabric. This object memory fabric instruction set can include trigger instructions defined in metadata for a particular memory object. Each trigger instruction can comprise a single instruction and action based on reference to a specific object to initiate or perform defined actions such as pre-fetching other objects or executing a trigger program.

Object memory data flow triggers

Embodiments of the invention provide systems and methods for managing processing, memory, storage, network, and cloud computing to significantly improve the efficiency and performance of processing nodes. Embodiments can implement an object memory fabric including object memory modules storing memory objects created natively within the object memory module and may be a managed at a memory layer. The memory module object directory may index all memory objects within the object memory module. A hierarchy of object routers communicatively coupling the object memory modules may each include a router object directory that indexes all memory objects and portions contained in object memory modules below the object router in the hierarchy. The hierarchy of object routers may behave in aggregate as a single object directory communicatively coupled to all object memory modules and to process requests based on the router object directories.

Distributed index for fault tolerant object memory fabric

According to one embodiment, a hardware-based processing node of a plurality of hardware-based processing nodes in an object memory fabric can comprise a memory module storing and managing a plurality of memory objects in a hierarchy of the object memory fabric. Each memory object can be created natively within the memory module, accessed using a single memory reference instruction without Input/Output (I/O) instructions, and managed by the memory module at a single memory layer. The object memory fabric can utilize a memory fabric protocol between the hardware-based processing node and one or more other nodes of the plurality of hardware-based processing nodes to distribute and track the memory objects across the object memory fabric. The memory fabric protocol can be utilized across a dedicated link or across a shared link between the hardware-based processing node and one or more other nodes of the plurality of hardware-based processing nodes.

Object memory interfaces across shared links

Growing community of inventors

Boulder, CO, United States of America

Steven J Frank