Comparison of the efficiency of our ultra-light MTANN model with that of the traditional state-of-the-art model. (IMAGE)

Institute of Science Tokyo

Comparison of the efficiency of our ultra-light MTANN model with that of the traditional state-of-the-art model.

Caption

Despite being trained on a significantly lower-computational setup (MacBook Air with M1 chip), the 3D Massive-Training Artificial Neural Network (MTANN) achieves superior performance (area under the curve (AUC) = 0.92), faster inference, and drastically reduced training time and parameter count compared to that of 3D ResNet.

Credit

Kenji Suzuki from Institute of Science Tokyo, Japan

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