Breast cancer accounts for about 30% of all diagnosed cancers in women, and is the second leading cause of death in women worldwide. Mammography is currently the most common modality for screening and detecting breast cancer. However, breast lesions found in mammograms are often benign. To improve the specificity, doctors often examine suspicious lesions using ultrasound (US) imaging. Ultrasound is also known to increase cancer detection sensitivity, in particular for women with dense breasts. However, it is an operator-dependent modality, and US image interpretation varies depending on the expertise of the radiologist. In order to reduce operator-dependent diagnosis variability and increase diagnosis accuracy, computer-aided detection and diagnosis (CAD) systems have been developed for breast cancer detection and classification. CAD systems typically perform image enhancement, region-of-interest (ROI) detection, feature extraction from ROIs, and classification. Unfortunately, US CAD efficacy is often limited by incorrect automatic detection and localization of lesions, and a lack of robustness of calculated features.
In one aspect of the invention a method is provided for detecting regions-of-interest in medical images by identifying one or more image features in one or more medical images of a subject patient, identifying one or more clinical descriptors within clinical records of the subject patient, and identifying, using a visual-textual relationship model, regions-of-interest within the medical images of the subject patient based on relationships within the visual-textual relationship model corresponding to relationships between the image features identified in the subject patient medical images and the clinical descriptors identified in the subject patient clinical records.
In other aspects of the invention systems and computer program products embodying the invention are provided.
Aspects of the invention will be understood and appreciated more fully from the following detailed description taken in conjunction with the appended drawings in which:
Embodiments of the invention may include a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the invention.
Aspects of the invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
Reference is now made to
Clinical records of the patient are provided in a computer-readable text format to a computer-based text analyzer 102, which identifies clinical descriptors within the clinical records in accordance with conventional techniques, such as in accordance with the methods described by P. Kisilev, S. Hashoul, E. Walach, and A. Tzadok in “Lesion classification using clinical and visual data fusion by multiple kernel learning,” SPIE Medical Imaging 2014, (hereinafter “Kisilev2”).
The image features identified by image analyzer 100, as well as the clinical descriptors identified by text analyzer 102, are provided in a computer-readable format to a computer-based model builder 104 which identifies relationships between the image features and the clinical descriptors and builds a visual-textual relationship model 106 of these relationships. Model builder 104 preferably employs Multiple Kernel Learning to train a Support Vector Machine classifier, such as in accordance with the methods described by Kisilev2, where parameters of the kernels and the weights of the kernels are trained on a set of training images such that the trained parameters minimize any expected error, and where a weight represents an importance value associated with a type of image feature or a reliability value for characterizing an image feature in a correct manner and for discriminating the specific type of image feature from the other types of image features.
Model builder 104 is optionally configured to utilize other relationships 108 between image features and clinical protocols to create within visual-textual relationship model 106 new relationships between clinical descriptors and the clinical protocols. For example, if there is a known relationship associating bright image features with the use of a particular malignancy detector that is suited to the analysis of images having bright image features, if a clinical descriptor in the clinical records, such as the word “fever,” is determined to be correlated with bright image features, then model builder 104 preferably creates within visual-textual relationship model 106 a relationship between the clinical descriptor “fever” and the clinical protocol of using the particular malignancy detector.
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As shown, the techniques for controlling access to at least one resource may be implemented in accordance with a processor 310, a memory 312, I/O devices 314, and a network interface 316, coupled via a computer bus 318 or alternate connection arrangement.
It is to be appreciated that the term “processor” as used herein is intended to include any processing device, such as, for example, one that includes a CPU (central processing unit) and/or other processing circuitry. It is also to be understood that the term “processor” may refer to more than one processing device and that various elements associated with a processing device may be shared by other processing devices.
The term “memory” as used herein is intended to include memory associated with a processor or CPU, such as, for example, RAM, ROM, a fixed memory device (e.g., hard drive), a removable memory device (e.g., diskette), flash memory, etc. Such memory may be considered a computer readable storage medium.
In addition, the phrase “input/output devices” or “I/O devices” as used herein is intended to include, for example, one or more input devices (e.g., keyboard, mouse, scanner, etc.) for entering data to the processing unit, and/or one or more output devices (e.g., speaker, display, printer, etc.) for presenting results associated with the processing unit.
The descriptions of the various embodiments of the invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.