# US 11,308,611 B2 · Reducing false positive detections of malignant lesions using multi-parametric magnetic resonance imaging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,611 B2 - Document ID: US11308611B2 - Status: Granted - Kind: B2 - Application number: 16/782,201 - Filed: 2020-02-05 - Published: 2021-04-15 - Granted: 2022-04-19 - Earliest claimed: 2019-10-09 - Assignees: Siemens Healthcare GmbH - Applicants: Siemens Healthcare GmbH - Inventors: Xin Yu; Bin Lou; Bibo Shi; David Jean Winkel; Ali Kamen; Dorin Comaniciu - Web page: https://patentgrep.com/patent/US11308611B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06F 18/253: {of extracted features} - G06V 10/451: {with interaction between the filter responses, e.g. cortical complex cells} - G06V 10/764: using classification, e.g. of video objects - G06V 10/806: {of extracted features} - G06V 10/82: using neural networks - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30081: Prostate - G06T 2207/30096: Tumor; Lesion - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems and methods for reducing false positive detections of malignant lesions are provided. A candidate malignant lesion is detected in one or more medical images, such as, e.g., multi-parametric magnetic resonance images. One or more patches associated with the candidate malignant lesion are extracted from the one or more medical images. The candidate malignant lesion is classified as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extract patches using a trained machine learning network. The results of the classification are output. ## Claims ### Claim 1 A method comprising: extracting one or more patches associated with a candidate malignant lesion from one or more medical images, the candidate malignant lesion representing a lesion detected as being malignant; classifying the candidate malignant lesion as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extracted patches using a trained machine learning network; and outputting results of the classification. ### Claim 2 (depends on 1) The method of claim 1, wherein extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: extracting a plurality of patches having different fields of view from the one or more medical images. ### Claim 3 (depends on 2) The method of claim 2, wherein extracting a plurality of patches having different fields of view from the one or more medical images comprises: cropping the one or more medical images at different dimensions. ### Claim 4 (depends on 1) The method of claim 1, wherein extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: extracting a patch depicting the candidate malignant lesion from a particular image of the one or more medical images. ### Claim 5 (depends on 4) The method of claim 4, wherein extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: extracting patches from images of the one or more medical images that neighbor the particular image. ### Claim 6 (depends on 1) The method of claim 1, further comprising: detecting the candidate malignant lesion in the one or more medical images using a machine learning based detection network. ### Claim 7 (depends on 1) The method of claim 1, wherein the one or more medical images comprises multi-parametric magnetic resonance images. ### Claim 8 An apparatus, comprising: means for extracting one or more patches associated with a candidate malignant lesion from one or more medical images, the candidate malignant lesion representing a lesion detected as being malignant; means for classifying the candidate malignant lesion as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extracted patches using a trained machine learning network; and means for outputting results of the classification. ### Claim 9 (depends on 8) The apparatus of claim 8, wherein the means for extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: means for extracting a plurality of patches having different fields of view from the one or more medical images. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the means for extracting a plurality of patches having different fields of view from the one or more medical images comprises: means for cropping the one or more medical images at different dimensions. ### Claim 11 (depends on 8) The apparatus of claim 8, wherein the means for extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: means for extracting a patch depicting the candidate malignant lesion from a particular image of the one or more medical images. ### Claim 12 (depends on 11) The apparatus of claim 11, wherein the means for extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: means for extracting patches from images of the one or more medical images that neighbor the particular image. ### Claim 13 (depends on 8) The apparatus of claim 8, further comprising: means for detecting the candidate malignant lesion in the one or more medical images using a machine learning based detection network. ### Claim 14 (depends on 8) The apparatus of claim 8, wherein the one or more medical images comprises multi-parametric magnetic resonance images. ### Claim 15 A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising: extracting one or more patches associated with a candidate malignant lesion from one or more medical images, the candidate malignant lesion representing a lesion detected as being malignant; classifying the candidate malignant lesion as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extracted patches using a trained machine learning network; and outputting results of the classification. ### Claim 16 (depends on 15) The non-transitory computer readable medium of claim 15, wherein extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: extracting a plurality of patches having different fields of view from the one or more medical images. ### Claim 17 (depends on 16) The non-transitory computer readable medium of claim 16, wherein extracting a plurality of patches having different fields of view from the one or more medical images comprises: cropping the one or more medical images at different dimensions. ### Claim 18 (depends on 15) The non-transitory computer readable medium of claim 15, wherein extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: extracting a patch depicting the candidate malignant lesion from a particular image of the one or more medical images. ### Claim 19 (depends on 18) The non-transitory computer readable medium of claim 18, wherein extracting one or more patches associated with a candidate malignant lesion from one or more medical images comprises: extracting patches from images of the one or more medical images that neighbor the particular image. ### Claim 20 (depends on 15) The non-transitory computer readable medium of claim 15, further comprising: detecting the candidate malignant lesion in the one or more medical images using a machine learning based detection network.