Industrial Technology Research Institute

Ming-Shan Deng

Ya-Wen Lee

Po-Yi Wu

Shih-Fang Chang

Jiazheng Zhou

Xuan-Yi Lin

Jung-Tzu Liu

Ting-Yuan Wang

A medical image analysis method includes: reading an original medical image; performing image classification and object detection on the original medical image to generate a first classification result and a plurality of object detection results by a plurality of complementary artificial intelligence (AI) models; performing object feature integration and transformation on a first detection result and a second detection result among the object detection results to generate a transformation result by a features integration and transformation module; and performing machine learning on the first classification result and the transformation result to generate an image interpretation result by a machine learning module and display the image interpretation result.

Medical image analysis method and device

A training system and method of object detection model is disclosed. The training system includes an object detection model and a loss calculation module. The object detection model is configured to generate an output image according to an input image. The loss calculation module, coupled to the object detection model, is configured to calculate a total classification loss value according to the output image and a solution image, calculate a loss value according to the total classification loss value, and transmit the loss value to the object detection model. The total classification loss value is calculated according to a number of classification losses corresponding to a number of object types. Each classification loss corresponding to each object type is calculated according to a first parameter, a second parameter and a third parameter.

Training method and system of objects detection model based on adaptive annotation design

A neural network model fusion method and an electronic device using the same are provided. The neural network model fusion method includes the following steps. An image is received. The image is analyzed through several neural network models. The neural network models include at least two of a degree classification model, a multi-class identification model and an object detection model. Several analysis results are obtained according to the neural network models. These analysis results are converted into a number of conversion factors. The conversion factors are inputted into a fusion model to obtain a fusion result.

Neural network model fusion method and electronic device using the same

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Taichung, Taiwan

Ming-Shan Deng