Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A method for adjusting a convolutional neural network includes following operations. The convolutional neural network includes convolution layers in a sequential order. Receptive field widths of the convolution layers in a first model of the convolutional neural network are determined. Channel widths of the convolution layers in the first model are reduced into reduced channel widths according to the receptive field widths of the convolution layers and an input image width. A structure of a second model of the convolutional neural network is formed according to the reduced channel widths. The second model of the convolutional neural network is trained according the structure of the second model.

Abstract: A training task optimization system includes a processor. The processor is configured to receive training environment information of a training task. The training environment information at least carries information corresponding to training samples in the training task. The processor is configured to calculate a memory distribution for the training task based on memory factors, the training samples and a neural network, and select a mini-batch size that is fit to the memory distribution. In response to the training environment information, the processor is configured to output the mini-batch size for execution of the training task.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: An image processing training method includes the following steps. A template label image is obtained, in which the template label image comprises a label corresponding to a target. A plurality of first reference images are obtained, in which each of the first reference images comprises object image data corresponding to the target. A target image according to the template label image and the first reference images is generated, in which the target image comprises a generated object, a contour of the generated object is generated according to the template label image, and a color or a texture of the target image is generated according to the first reference images.

Abstract: An image processing method includes generating, by a processing component, a first input feature map based on an input image using a first convolutional neural network; generating, by the processing component, a first template feature map based on a template image using the first convolutional neural network; generating, by the processing component, a first estimated motion parameter based on an initial motion parameter, the first input feature map and the first template feature map using an iterative Lucas-Kanade network; and performing, by the processing component, image alignment between the input image and the template image based on the first estimated motion parameter.

Abstract: A computing device for training a fully-connected neural network (FCNN) comprises at least one storage device; and at least one processing circuit, coupled to the at least one storage device. The at least one storage device stores, and the at least one processing circuit is configured to execute instructions of: computing a block-diagonal approximation of a positive-curvature Hessian (BDA-PCH) matrix of the FCNN; and computing at least one update direction of the BDA-PCH matrix according to an expectation approximation conjugated gradient (EA-CG) method.

Abstract: A method for adjusting a convolutional neural network includes following operations. The convolutional neural network includes convolution layers in a sequential order. Receptive field widths of the convolution layers in a first model of the convolutional neural network are determined. Channel widths of the convolution layers in the first model are reduced into reduced channel widths according to the receptive field widths of the convolution layers and an input image width. A structure of a second model of the convolutional neural network is formed according to the reduced channel widths. The second model of the convolutional neural network is trained according the structure of the second model.

Abstract: A machine learning system includes a memory and a processor. The processor is configured to access and execute at least one instruction from the memory to perform inputting raw data to a first partition of a neural network, in which the first partition at least comprises an activation function of the neural network. The activation function is applied to convert the raw data into irreversible metadata. The metadata is transmitted to a second partition of the neural network as inputs to generate a learning result corresponding to the raw data.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A training task optimization system includes a processor. The processor is configured to receive training environment information of a training task. The training environment information at least carries information corresponding to training samples in the training task. The processor is configured to calculate a memory distribution for the training task based on memory factors, the training samples and a neural network, and select a mini-batch size that is fit to the memory distribution. In response to the training environment information, the processor is configured to output the mini-batch size for execution of the training task.

Abstract: An image processing training method includes the following steps. A template label image is obtained, in which the template label image comprises a label corresponding to a target. A plurality of first reference images are obtained, in which each of the first reference images comprises object image data corresponding to the target. A target image according to the template label image and the first reference images is generated, in which the target image comprises a generated object, a contour of the generated object is generated according to the template label image, and a color or a texture of the target image is generated according to the first reference images.

Abstract: An image processing method includes generating, by a processing component, a first input feature map based on an input image using a first convolutional neural network; generating, by the processing component, a first template feature map based on a template image using the first convolutional neural network; generating, by the processing component, a first estimated motion parameter based on an initial motion parameter, the first input feature map and the first template feature map using an iterative Lucas-Kanade network; and performing, by the processing component, image alignment between the input image and the template image based on the first estimated motion parameter.

Abstract: A medical system includes an interaction interface and an analysis engine. The interaction interface is configured for receiving an initial symptom. The analysis engine is communicated with the interaction interface. The analysis engine includes a prediction module. The prediction module is configured for generating symptom inquiries to be displayed on the interaction interface according to a prediction model and the initial symptom. The interaction interface is configured for receiving responses corresponding to the symptom inquiries. The prediction module is configured to generate a result prediction according to the prediction model, the initial symptom and the responses.

Abstract: The disclosure provides a method, an electronic apparatus, and a computer readable medium of constructing a classifier for skin-infection detection. The method includes the following steps. A codebook of representative features is constructed based on a plurality of target-disease-irrelevant images. Transfer-learned disease features are extracted from target-disease images according to the codebook without any medical domain knowledge, where the target-disease images are captured by at least one image capturing device. Supervised learning is performed based on the transfer-learned target-disease features to train the classifier for skin-infection detection.

Abstract: The disclosure provides a method, an electronic apparatus, and a computer readable medium of constructing a classifier for skin-infection detection. The method includes the following steps. A codebook of representative features is constructed based on a plurality of target-disease-irrelevant images. Transfer-learned disease features are extracted from target-disease images according to the codebook without any medical domain knowledge, where the target-disease images are captured by at least one image capturing device. Supervised learning is performed based on the transfer-learned target-disease features to train the classifier for skin-infection detection.