Abstract: The present invention disclose a medical image-based system for predicting lesion classification and a method thereof. The system comprises a feature data extracting module for providing a raw feature data based on a medical image, and a predicting module for outputting a predicted class and a risk index according to the raw feature data. The predicting module comprises a classification unit for generating the predicted class and a prediction score corresponding thereto according to the raw feature data, and a risk evaluation unit for generating the risk index according to the prediction score. The system provides medical personnels a reference score and a risk index to determine progression of a certain disease.

Abstract: A metal-and-resin composite includes an aluminum or aluminum alloy substrate, an aluminum oxide film on the aluminum or aluminum alloy substrate, and at least one resin article coupled to the aluminum oxide film. The aluminum or aluminum alloy substrate defines a plurality of corrosion pores, and the aluminum oxide film defines a plurality of nano-pores. Some of the nano-pores extend through the aluminum oxide film and couple to the corrosion pores. Some parts of the resin article fill in the nano-pores and the corrosion pores, thus greatly improving bond between the resin article and the aluminum or aluminum alloy substrate.

Abstract: An aluminum alloy-and-resin composite includes an aluminum alloy substrate and resin composition formed on the substrate. The substrate is subjected to electrochemically etched and formed with nano-pores on its surface. The resin composition integrally couples to the surface of the aluminum alloy substrate by filling the nano-pores. The resin composition contains crystalline thermoplastic synthetic resins.