Abstract: A system for training transfer models that includes a memory and a processor coupling to each other. The memory stores instructions. The processor accesses the instructions to retrieve raw datasets from a first-type device and train a feature extraction model, a transfer feature and a preliminary classify model based on the raw datasets. The processor further transfers the feature extraction model, the transfer feature and the preliminary classify model to a host of a manufacturer so that the host directly applies the feature extraction model and the transfer feature, and retrains the preliminary classify model as an adapted classify model based on practical datasets.

Abstract: A computer-aided recognition system for treatment response of rectal cancer is provided to predict the probability of a pathological complete response (pCR) of a rectal cancer patient after preoperative neoadjuvant chemoradiotherapy. The system includes a random forest model having several decision trees. Each decision tree has at least a feature node. Each feature node generates two branches according to a feature threshold, wherein each branch connects to another feature node or corresponds to an elementary pCR predicting probability. The random forest model integrates the elementary pCR probability of each decision tree, so as to generate a final pCR probability.

Abstract: A system for training transfer models that includes a memory and a processor coupling to each other. The memory stores instructions. The processor accesses the instructions to retrieve raw datasets from a first-type device and train a feature extraction model, a transfer feature and a preliminary classify model based on the raw datasets. The processor further transfers the feature extraction model, the transfer feature and the preliminary classify model to a host of a manufacturer so that the host directly applies the feature extraction model and the transfer feature, and retrains the preliminary classify model as an adapted classify model based on practical datasets.

Abstract: A prediction system of tumor image-aided prediction of prognosis for patients with uterine cervical cancer is provided. The prediction system includes a data augmentation module and a deep convolution neural network model. The data augmentation module is used to apply a data expansion process of the image data, so as to generate a plurality of slice sets of the uterine cervical cancer tumor. The deep convolution neural network model is used to apply a feature analysis to the slices, so as to predict the prognosis of a patient after chemoradiotherapy.

Abstract: A computer-aided recognition system for treatment response of rectal cancer is provided to predict the probability of a pathological complete response (pCR) of a rectal cancer patient after preoperative neoadjuvant chemoradiotherapy. The system includes a random forest model having several decision trees. Each decision tree has at least a feature node. Each feature node generates two branches according to a feature threshold, wherein each branch connects to another feature node or corresponds to an elementary pCR predicting probability. The random forest model integrates the elementary pCR probability of each decision tree, so as to generate a final pCR probability.

Abstract: A prediction system of tumor image-aided prediction of prognosis for patients with uterine cervical cancer is provided. The prediction system includes a data augmentation module and a deep convolution neural network model. The data augmentation module is used to apply a data expansion process of the image data , so as to generate a plurality of slice sets of the uterine cervical cancer tumor. The deep convolution neural network model is used to apply a feature analysis to the slices, so as to predict the prognosis of a patient after chemoradiotherapy.

Abstract: This invention relates to an epoxy resin composition which comprises polyalkyl phenol resins or polyalkyl phenol epoxy resins or both, wherein the polyalkyl phenol resins are represented by formula (I): ##STR1## wherein Ra, Rb and Rc may be the same or different from each other and stand for C.sub.1-5 alkyl styryl or halogen respectively;n is an integer of 0 to 4;k is an integer of 0 to 3;l is an integer of 0 to 4; andm is an integer of 1 to 10;and the polyalkyl phenol epoxy resins, which are obtained by reacting polyalkyl phenol resins of formula (I) with epihalohydrins, are represented by formula (II): ##STR2## wherein G stands for ##STR3## while the other symbols are the same as those defined above. The epoxy resin composition of this invention is able to provide a reactivity at lower temperatures and provide lower viscosity when used as hardener resins, as compared with traditional phenolic novolacs.