# US 12,602,782 B2 · Image processing device, image processing method, and storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,782 B2 - Document ID: US12602782B2 - Status: Granted - Kind: B2 - Application number: 18/409,907 - Filed: 2024-01-11 - Published: 2024-06-27 - Granted: 2026-04-14 - Earliest claimed: 2022-12-27 - Assignees: NEC Corporation - Applicants: NEC Corporation - Inventors: Yuji Iwadate - Web page: https://patentgrep.com/patent/US12602782B2 ## CPC - G06T 7/00: Image analysis - G06T 7/0012: {Biomedical image inspection} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06T 2207/10068: Endoscopic image - G06T 2207/30096: Tumor; Lesion - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The image processing device 1X includes a first acquisition means 30X, a second acquisition means 31X, and an inference means 33X. The first acquisition means 30X acquires a set value of a first index indicating an accuracy relating to a lesion analysis. The second acquisition means 31X acquires, for each of plural models which make inference regarding a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied. The inference means 33X makes inference regarding the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index and the plural models. ## Claims ### Claim 1 An image processing device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: acquire a set value of a first index indicating an accuracy relating to a lesion analysis; acquire, for each of plural models which infer a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied; and infer the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index, the plural models, and a threshold value for the lesion associated with a combination of the set value of the first index and the predicted value of the second index. ### Claim 2 (depends on 1) The image processing device according to claim 1, is wherein the second index has a trade-off relation with the first index. ### Claim 3 (depends on 1) The image processing device according to claim 1, wherein a combination of the first index and the second index is a combination of sensitivity and a false positive rate or a combination of sensitivity and the precision. ### Claim 4 (depends on 1) The image processing device according to claim 1, wherein, for each of plural models, the at least one processor is further configured to execute the instructions to: acquire correspondence information indicative of a correspondence relation between the first index and the second index, and acquire, as the predicted value, a value of the second index corresponding to the set value according to the correspondence information. ### Claim 5 (depends on 4) The image processing device according to claim 4, wherein the correspondence information indicates an ROC curve, an LROC curve, an FROC curve, or a PR curve. ### Claim 6 (depends on 4) The image processing device according to claim 4, wherein curves of the plural models on two-dimensional coordinates with the first index and the second index as coordinate axes include intersecting points at which the curves intersect each other. ### Claim 7 (depends on 1) The image processing device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: select a model with a best accuracy indicated by the predicted value from the plural models, and infer the lesion based on the selected model and the endoscopic image. ### Claim 8 (depends on 1) The image processing device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: generate an inference result obtained by weighting, based on the predicted value, each inference result based on the endoscopic image by the plural models. ### Claim 9 (depends on 1) The image processing device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: execute a first mode or a second mode while switching between the first mode and the second mode based on a predetermined condition, wherein, in the first mode, the at least one processor is further configured to execute the instructions to: select a model with a best accuracy indicated by the predicted value from the plural models, and infer the lesion based on the selected model and the endoscopic image, and wherein, in the second mode, the at least one processor is configured to execute the instructions to generate an inference result obtained by weighting, based on the predicted value, each inference result based on the endoscopic image by the plural models. ### Claim 10 (depends on 9) The image processing device according to claim 9, wherein the at least one processor is further configured to execute the instructions to: execute the first mode or the second mode while switching between the first mode and the second mode based on whether or not a presence or absence of detection of a disorder other than a target disorder of the lesion analysis. ### Claim 11 (depends on 9) The image processing device according to claim 9, wherein the at least one processor is further configured to execute the instructions to: execute the first mode or the second mode while switching between the first mode and the second mode based on an external input. ### Claim 12 (depends on 1) The image processing device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: select a predetermined number of models with top accuracies indicated by the predicted value from the plural models, and generates an inference result obtained by weighting, based on the predicted value, each inference result by the predetermined number of models. ### Claim 13 (depends on 1) The image processing device according to claim 1, wherein each of the plural model is a model obtained through a machine learning in which sets of the endoscopic image and correct answer data are used as training data, the correct answer data indicating an inference result to be outputted by the model when the endoscopic image is inputted to the model. ### Claim 14 (depends on 1) The image processing device according to claim 1, wherein the at least one processor is further configured to further execute the instructions to: output an inference result by a display device or an audio output device to support examiner's decision making. ### Claim 15 An image processing method executed by a computer, the image processing method comprising: acquiring a set value of a first index indicating an accuracy relating to a lesion analysis; acquiring, for each of plural models which infer a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied; and inferring the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index, the plural models, and a threshold value for the lesion associated with a combination of the set value of the first index and the predicted value of the second index. ### Claim 16 A non-transitory computer readable storage medium storing a program executed by a computer, the program causing the computer to: acquire a set value of a first index indicating an accuracy relating to a lesion analysis; acquire, for each of plural models which infer a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied; and infer the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index, the plural models, and a threshold value for the lesion associated with a combination of the set value of the first index and the predicted value of the second index.