Patent ID: 11948297
Assignee: MEDCOGNETICS, INC.
Field: Medical technology (Instruments)
Classification: CPC G  A | IPC A  G

Claim 15:
16. A system comprising:
a convolutional neural network comprising a plurality of layers arranged in a hierarchy, wherein an input of the convolutional neural network is at a bottom of the hierarchy of layers and an output is at a top of the hierarchy of layers;
a first plurality of layers of the convolutional neural network which is trained by machine learning and not active learning using a first set of training data comprising first breast images comprising multiple ethnicities;
a second layer, trained by active learning using a second set of training data comprising second breast images from only a different single ethnicity, a first ethnicity;
a third layer, trained by active learning using a third set of training data comprising third images from only a single ethnicity, a second ethnicity that is different from the first ethnicity; and
a fourth layer, trained by active learning using a fourth set of training data comprising fourth images from only a single ethnicity, a third ethnicity that is different from the first and second ethnicities,
wherein the second, third, and fourth layers are at the same level in the hierarchy of the convolutional neural network, and the second and third layers are above the first layers of the convolutional neural network,
each of the first layers, second layer, third layer, and fourth layer has a single vote, and a diagnosis probability of breast cancer is based on a majority of the votes such that any three of the first layers, second layer, third layer, and fourth layer would constitute a majority, and
when a majority of the votes is not obtained, the diagnosis probability of breast cancer is based on only the first layers,
a fifth layer and sixth layer are within the first layers of the convolutional neural network, and the sixth layer is above the fifth layer,
the breast image for analysis and an optional identification of an ethnicity of the breast image are input to the fifth layer, the fifth layer extracts first different features in the breast image, the sixth layer stores first weightings for the first different features in the breast image extracted by the fifth layer,
when the ethnicity of the breast image for analysis is the first ethnicity, the first weightings for the first different features are input to the second layer and not the third or fourth layers, and given the first weightings from the sixth layer, the second layer outputs second weightings for second different features,
a fifth layer and sixth layer are within the first layers of the convolutional neural network, and the sixth layer is above the fifth layer,
when the ethnicity of the breast image for analysis is the second ethnicity, the first weightings for the first different features are input to the third layer and not the second or fourth layers, and given the first weightings from the sixth layer, the third layer outputs third weightings for third different features,
when the ethnicity of the breast image for analysis is the third ethnicity, the first weightings for the first different features are input to the fourth layer and not the second or third layers, and given the first weightings from the sixth layer, the fourth layer outputs fourth weightings for fourth different features, and
when the ethnicity of the breast image for analysis is not identified, the first weightings for the first different features are input to the second, third, and fourth layers, given the first weightings from the sixth layer, the second layer outputs second weightings for the second different features, given the first weightings from the sixth layer, the third layer outputs third weightings for the third different features, and given the first weightings from the sixth layer, the fourth layer outputs fourth weightings for the fourth different features.