Institute for Information Industry

Yao-Min Huang

Wen-Shan Liou

Kai-Hsuan Chan

Hsin-I Lai

Chung-Che Wang

Pei-Yu Liao

A method for processing breast tomosynthesis images includes: obtaining multiple breast images, performing binarization operation and pooling operation to at least one marked breast image to obtain a lesion mask; training a convolutional neural network (CNN) according to the breast images and the lesion mask; obtaining multiple cropped images from the breast images of different layers according to a particular two-dimensional position, and inputting each cropped image into the CNN to obtain cropped heat maps; inputting the cropped heat maps into a recurrent neural network (RNN) in an order of the levels to obtain output heat maps, and combining the output heat maps to obtain a integrated heat map, and training the RNN according to the integrated heat map and the lesion mask.

Method and electric device for processing breast Tomosynthesis images

A recognition system includes an image capturing device and a server. The image capturing device generates a Mth-layer calculation result based on image data and a convolutional neural network (CNN), and transmits feature information associated with the Mth-layer calculation result. M is a positive integer, M is equal to or greater than 1, M is less than or equal to N, and N is a predetermined positive integer. The server receives the feature information. The server generates a Kth-layer calculation result based on the feature information and the CNN by an iterative method when M is less than N. K is a positive integer which is greater than M and less than or equal to N. The server generates a first recognition result associated with the image data based on the Kth-layer calculation result and a first recognition model when K is equal to N.

System, method, and non-transitory computer readable storage medium for image recognition based on convolutional neural networks

A computer system is configured for performing audio matching. The computer system includes a client terminal and a server. The client terminal includes a classifier, a first landmark extraction module, a first encoder module and an integration module. The classifier is configured for estimating an identification difficulty. The first landmark extraction module is configured for determining the number of times for landmark extraction, and for extracting first landmarks. The first encoder module is configured for encoding the first landmarks. The server includes a second landmark extraction module, a second encoder module, a hash table, a table-lookup module, an analysis module and a decision module. The second landmark extraction module is configured for extracting second landmarks. The second encoder module is configured for encoding the second landmarks. The table-lookup module is configured for performing a table lookup. The decision module is configured for determining an audio matching result.

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Taipei, Taiwan

Yao-Min Huang