Machine learning device and method

Patent metadata

Field	Value
Publication number	US-2020387751-A1
Application number	US-202017000372-A
Country	US
Kind code	A1
Filing date	Aug 24, 2020
Priority date	Feb 27, 2018
Publication date	Dec 10, 2020
Grant date	—

Abstract

There are provided machine learning device and method which can prepare divided data suitable for machine learning from volume data for learning. A machine learning unit (15) calculates detection accuracy of each organ O(j,i) in a predicted mask Pj using a loss function Loss. However, the detection accuracy of the organ O(k,i) with a volume ratio A(k,i)<Th is not calculated. That is, in the predicted mask Pk, the detection accuracy of the organ O(k,i) with a volume ratio that is small to some extent is ignored. The machine learning unit (15) changes each connection load of a neural network (16) from an output layer side to an input layer side according to the loss function Loss.

First claim

What is claimed is:

1 . A machine learning device comprising:
a learning data input unit that receives an input of learning data including volume data of a tomographic image and labeling of a region in the volume data; a division unit that divides the learning data of which the input is received by the learning data input unit to create divided learning data; a learning exclusion target region discrimination unit that discriminates a learning exclusion target region which is a region to be excluded from a target of learning, from the divided learning data created by the division unit and the learning data; and a machine learning unit that performs machine learning of labeling of a region other than the learning exclusion target region discriminated by the learning exclusion target region discrimination unit, on the basis of the divided learning data created by the division unit.

2 . The machine learning device according to claim 1 ,
wherein the learning exclusion target region discrimination unit compares a volume of the region labeled in the divided learning data created by the division unit and a volume of the region labeled in the learning data and discriminates the learning exclusion target region according to whether the volume is equal to or less than a threshold value.

3 . The machine learning device according to claim 1 , further comprising:
a detection accuracy calculation unit that calculates detection accuracy of a region other than the learning exclusion target region discriminated by the learning exclusion target region discrimination unit, wherein the machine learning unit performs machine learning of the labeling of the region other than the learning exclusion target region on the basis of the divided learning data created by the division unit and the detection accuracy calculated by the detection accuracy calculation unit.

4 . The machine learning device according to claim 3 ,
wherein the detection accuracy calculation unit calculates the detection accuracy on the basis of an average of Intersection over Union (IoU) between a predicted label and a ground truth label of each region.

5 . The machine learning device according to claim 1 ,
wherein the division unit re-divides the learning data such that the entire learning exclusion target region is included.

6 . The machine learning device according to claim 1 ,
wherein the division unit creates pieces of divided learning data having an overlapping portion.

7 . The machine learning device according to claim 1 ,
wherein the tomographic image is a three-dimensional medical tomographic image, and the region includes an organ.

8 . A machine learning method executed by a computer, the machine learning method comprising:
a step of receiving an input of learning data including volume data of a tomographic image and labeling of a region in the volume data; a step of dividing the learning data to create divided learning data; a step of discriminating a learning exclusion target region which is a region to be excluded from a target of learning, from the divided learning data and the learning data; and a step of performing machine learning of labeling of a region other than the learning exclusion target region on the basis of the divided learning data.

9 . A machine-learned model obtained by machine learning by the machine learning method according to claim 8 .

10 . A non-transitory computer-readable recording medium that records thereon, computer commands which cause a computer to execute the machine learning method according to claim 8 in a case where the computer commands are read by the computer.

Assignees

• Fujifilm Corp

Inventors

• Keshwani Deepak

Classifications

• G06T12/00

  Tomographic reconstruction from projections · CPC title

• G06T12/30 Primary

  Image post-processing, e.g. metal artefact correction · CPC title

• G06V10/7747

  Organisation of the process, e.g. bagging or boosting · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V10/454

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title