Abstract: A method for standardizing image annotation includes receiving a defect pattern; marking an image according to the defect pattern to generate a first judgement result; marking the image according to the defect pattern to generate a second judgement result; comparing the first judgement result and the second judgement result to obtain a comparison result; and updating the defect pattern according to the comparison result to standardize the defect pattern. The method for standardizing image annotation of the present specification can improve the marking stability of the training data of a trained image recognition algorithm, thereby improving the accuracy of image recognition of the trained image recognition algorithm.

Abstract: A method for establishing a defect detection model and an electronic apparatus are provided. A first classification model is established based on a training sample set including a plurality of training samples. The training samples are respectively input to the first classification model to obtain a classification result of each training sample. A plurality of outlier samples that are classified incorrectly are obtained from the training samples based on the classification result. A part of outlier samples that are classified incorrectly is deleted from the training samples, and the remaining training samples are used as an optimal sample set. A second classification model is established based on the optimal sample set so as to perform a defect detection through the second classification model.

Abstract: A method for standardizing image annotation includes receiving a defect pattern; marking an image according to the defect pattern to generate a first judgement result; marking the image according to the defect pattern to generate a second judgement result; comparing the first judgement result and the second judgement result to obtain a comparison result; and updating the defect pattern according to the comparison result to standardize the defect pattern. The method for standardizing image annotation of the present specification can improve the marking stability of the training data of a trained image recognition algorithm, thereby improving the accuracy of image recognition of the trained image recognition algorithm.

Abstract: A large scale cell image analysis method and system are provided. The method includes: obtaining a cell image; performing a region segmentation process; and performing a feature calculation process. The region segmentation process includes: performing a statistical intensity algorithm according to the cell image to calculate a first threshold and a second threshold; dividing the cell image into a background region and a cell region according to the first threshold; performing an average intensity process according to the cell region to calculate a third threshold and a fourth threshold; and dividing the cell region into a cytoplasmic region and a nucleus region according to the third threshold and the fourth threshold. The feature calculation process calculates at least one feature at least according to the cell region, the nucleus region, and the cytoplasmic region.

Abstract: A large scale cell image analysis method and system are provided. The method includes: obtaining a cell image; performing a region segmentation process; and performing a feature calculation process. The region segmentation process includes: performing a statistical intensity algorithm according to the cell image to calculate a first threshold and a second threshold; dividing the cell image into a background region and a cell region according to the first threshold; performing an average intensity process according to the cell region to calculate a third threshold and a fourth threshold; and dividing the cell region into a cytoplasmic region and a nucleus region according to the third threshold and the fourth threshold. The feature calculation process calculates at least one feature at least according to the cell region, the nucleus region, and the cytoplasmic region.