Abstract: A structure with a photodiode, an HEMT and an SAW device includes a photodiode and an HEMT. The photodiode includes a first electrode and a second electrode. The first electrode contacts a P-type III-V semiconductor layer. The second electrode contacts an N-type III-V semiconductor layer. The HEMT includes a P-type gate disposed on an active layer. A gate electrode is disposed on the P-type gate. Two source/drain electrodes are respectively disposed at two sides of the P-type gate. Schottky contact is between the first electrode and the P-type III-V semiconductor layer, and between the gate electrode and the P-type gate. Ohmic contact is between the second electrode and the first N-type III-V semiconductor layer, and between one of the two source/drain electrodes and the active layer and between the other one of two source/drain electrodes and the active layer.

Abstract: Provided is a medical image analyzing system and a method thereof, which includes: acquiring a processed image having a segmentation label corresponding to a cancerous part of an organ (if present), generating a plurality of image patches therefrom, performing feature analysis on the image patches and model training to obtain prediction values, drawing a receiver operating characteristic curve using the prediction values, and determining a threshold with which determines whether the image patches are cancerous, so as to effectively improve the detection rate of, for example, pancreatic cancer.

Abstract: A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a second model of a first analysis module and a third model of a second analysis module, respectively, to obtain at least one first prediction value and at least one second prediction value corresponding to the patient image; and outputting a determined result based on the first prediction value and the second prediction value. Further, processes between the first model, the second model and the third model can be automated, thereby improving identification rate of pancreatic cancer.

Abstract: Provided is a medical image analyzing system and a method thereof, which includes: acquiring a processed image having a segmentation label corresponding to a cancerous part of an organ (if present), generating a plurality of image patches therefrom, performing feature analysis on the image patches and model training to obtain prediction values, drawing a receiver operating characteristic curve using the prediction values, and determining a threshold with which determines whether the image patches are cancerous, so as to effectively improve the detection rate of, for example, pancreatic cancer.