UCSB Expands Bandwidth to Increase ResNet Speed


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Accordingly, the slice-level average classification accuracy (90%) of the proposed ADN + DRAL framework is the highest among the listed benchmarking algorithms. Compared to the straightforward VGG-16, the proposed ADN uses multiple atrous convolutions to extract multiscale features. As shown in Fig.11, the proposed ADN outperforms the VGG-16 and produces the best average ACAs for the BACH (94.10%), CCG (92.05%) and UCSB (97.63%) datasets. The overall correct classification rate of all the testing images is adopted as the criterion for performance evaluation.

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The RN model is first trained, and then makes predictions on the original patch-level training set. The patches with maximum confidence level lower than 0.5 are removed from the training set. The patch removal and model fine-tuning are performed in alternating sequence. A fixed validation set annotated by pathologists is used to evaluate the performance of fine-tuned model. Using DRAL resulted in a decline in the number of mislabeled patches. As a result, the performance of the RN model on the validation set is gradually improved.

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Conventional deep neural network training with an end-to-end cost function is unable to exert control on, or to provide guarantees regarding the features extracted by the layers of a DNN. Thus, despite the pervasive impact of DNNs, there remain significant concerns regarding their interpretability and robustness. In this work, we develop a software framework in which end-to-end costs can be supplemented with costs which depend on layer-wise activations, permitting more fine-grained control of features.

The proposed ADN achieves multiscale feature extraction by combining the atrous convolutions and dense blocks. Due to the impressive performance of deep learning networks, researchers find it appealing for application to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of major challenges.