Fig. 3: Overview of the proposed end-to-end trainable multi-task architecture based on deep learning. | npj Digital Medicine

Fig. 3: Overview of the proposed end-to-end trainable multi-task architecture based on deep learning.

From: Effect of computer aided detection system on esophageal neoplasm diagnosis in varied levels of endoscopists

Fig. 3

The input image is processed through two branches, producing recognition and detection results. GAP, or global average pooling, is used as a standard deep learning operation. The detection subnetwork’s architecture is illustrated above. An endoscopic image passes through a six-layer deep network, generating six prediction results at different scales, which are then fused to produce the final prediction.

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