# US 12,762,849 B2 · Training data creation apparatus, method, and program, machine learning apparatus and method, learning model, and image processing apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,849 B2 - Document ID: US12762849B2 - Status: Granted - Kind: B2 - Application number: 18/179,329 - Filed: 2023-03-06 - Published: 2023-06-29 - Granted: 2026-10-13 - Earliest claimed: 2020-09-07 - Assignees: FUJIFILM Corporation - Applicants: FUJIFILM Corporation - Inventors: Takuya Tsutaoka - Web page: https://patentgrep.com/patent/US12762849B2 ## CPC - G06V 10/7747: {Organisation of the process, e.g. bagging or boosting} - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A training data creation apparatus, method, and program that can create training data for training a region extractor with the expected performance in a situation in which a plurality of ground-truth region masks have been assigned to a single image, a machine learning apparatus and method, a learning model, and an image processing apparatus are provided. A training data creation apparatus includes a first processor, in which a training sample acquisition unit of first processor acquires a single image and a plurality of first ground-truth region masks for the single image from a database as a single training sample. A ground-truth region mask combination unit generates a single second ground-truth region mask from the first ground-truth region masks included in the training sample. An output unit outputs, as training data, the pair of the single image included in the training sample and the combined second ground-truth region mask. ## Claims ### Claim 1 A training data creation apparatus comprising a first processor that creates training data for machine learning, wherein the first processor: acquires, as a single training sample, a single image and a plurality of first ground-truth region masks for the single image; generates a single second ground-truth region mask from the plurality of first ground-truth region masks; outputs, as training data, a pair of the single image and the single second ground-truth region mask; calculates a sample weighting such that the higher a degree of disagreement among the plurality of first ground-truth region masks is, the smaller the sample weighting of the training sample during machine learning is, and outputs, as training data, the pair of the single image and the single second ground-truth region mask together with the calculated sample weighting. ### Claim 2 (depends on 1) The training data creation apparatus according to claim 1, wherein the first processor acquires, as the plurality of first ground-truth region masks for the single image, ground-truth region masks each assigned to the single image by a plurality of evaluators. ### Claim 3 (depends on 1) The training data creation apparatus according to claim 1, wherein the first processor inputs the single image into each of a plurality of first region extractors trained by machine learning in advance using a ground-truth region mask of each of a plurality of evaluators, and acquires, as the plurality of first ground-truth region masks for the single image, a plurality of region extraction results outputted by the plurality of first region extractors. ### Claim 4 (depends on 1) The training data creation apparatus according to claim 1, wherein the sample weighting is a value in a range from 0 to 1, and the first processor calculates, as the sample weighting, a value obtained by subtracting a proportion of pixels in disagreement among the plurality of first ground-truth region masks from 1. ### Claim 5 (depends on 1) The training data creation apparatus according to claim 1, wherein the first processor further acquires diagnostic information for biological tissue, and generates the single second ground-truth region mask using the first ground-truth region masks matching the diagnostic information from among the plurality of first ground-truth region masks. ### Claim 6 (depends on 1) The training data creation apparatus according to claim 1, wherein the first processor acquires, as the single second ground-truth region mask, any of: a ground-truth region mask in which a ground-truth region is a region of a common portion of the plurality of first ground-truth region masks; a ground-truth region mask in which the ground-truth region is a region of a union of the plurality of first ground-truth region masks; a ground-truth region mask in which the ground-truth region is a region containing pixels determined to be the ground truth by a majority decision for each pixel in the plurality of first ground-truth region masks; a ground-truth region mask combined by averaging the plurality of first ground-truth region masks; and a first ground-truth region mask which is selected from the plurality of first ground-truth region masks and which has the ground-truth region of maximum or minimum area. ### Claim 7 (depends on 1) The training data creation apparatus according to claim 1, further comprising: a recording apparatus storing a training data set containing a plurality of the training data. ### Claim 8 (depends on 1) The training data creation apparatus according to claim 1, wherein the single image is a medical image and the plurality of first ground-truth region masks are ground-truth region masks indicating a region of interest each assigned to the medical image by a plurality of evaluators. ### Claim 9 (depends on 1) A machine learning apparatus comprising: a second processor and a second region extractor, wherein the second processor uses machine learning to train the second region extractor using the training data created by the training data creation apparatus according to claim 1. ### Claim 10 (depends on 9) The machine learning apparatus according to claim 9, wherein the second region extractor is a learning model configured as a convolutional neural network. ### Claim 11 (depends on 10) A trained learning model configured as the convolutional neural network, being the second region extractor trained by machine learning performed by the machine learning apparatus according to claim 10. ### Claim 12 (depends on 11) An image processing apparatus comprising the learning model according to claim 11. ### Claim 13 A training data creation method for creating training data for machine learning by a first processor performing processing comprising: a step of acquiring, as a single training sample, a single image and a plurality of first ground-truth region masks for the single image; a step of generating a single second ground-truth region mask from the plurality of first ground-truth region masks; a step of outputting, as training data, a pair of the single image and the single second ground-truth region mask; a step of calculating a sample weighting such that the higher a degree of disagreement among the plurality of first ground-truth region masks is, the smaller the sample weighting of the training sample during machine learning is, wherein the pair of the single image and the single second ground-truth region mask together with the calculated sample weighting are outputted as training data. ### Claim 14 (depends on 13) The training data creation method according to claim 13, wherein the step of acquiring the training sample further comprises acquiring diagnostic information for biological tissue, and the step of generating the single second ground-truth region mask comprises generating the single second ground-truth region mask using the first ground-truth region masks matching the diagnostic information from among the plurality of first ground-truth region masks. ### Claim 15 (depends on 13) A machine learning method for training a second region extractor by a second processor using machine learning using the training data created according to the training data creation method according to claim 13. ### Claim 16 (depends on 13) A machine learning method for training a second region extractor by a second processor using machine learning using the training data created according to the training data creation method according to claim 13, wherein during initial training, the machine learning of the second region extractor is performed with the sample weighting included in the training data being a fixed value, and as the machine learning progresses and the sample weighting approaches an original value from the fixed value, or when the machine learning reaches a reference level, the machine learning of the second region extractor is performed with the sample weighting switched from the fixed value to the original value. ### Claim 17 A non-transitory, computer-readable tangible recording medium which records thereon a training data creation program for causing, when read by a computer, the computer to achieve: a function of acquiring, as a single training sample, a single image and a plurality of first ground-truth region masks for the single image; a function of generating a single second ground-truth region mask from the plurality of first ground-truth region masks; a function of outputting, as training data, a pair of the single image and the single second ground-truth region mask; a function of calculating a sample weighting such that the higher a degree of disagreement among the plurality of first ground-truth region masks is, the smaller the sample weighting of the training sample during machine learning is, and a function of outputting, as training data, the pair of the single image and the single second ground-truth region mask together with the calculated sample weighting.