https://www.selleckchem.com/products/azd9291.html
To obtain complets that are highly descriptive to image compositions, a weakly supervised complet ranking algorithm is designed by quantifying the quality of each complet. The algorithm seamlessly encodes three factors the image-level quality discrimination, weakly supervised constraint, and complet geometry of each image. Based on the top-ranking complets, a novel multi-column convolutional neural network (CNN) called SDA-Net is designed, which supports input segments with arbitrary shapes. The key is a dual-aggregation mechanism that
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