AT-Cascade R-CNN: a novel attention-based cascade R-CNN model for ovarian cancer lesion identification Online publication date: Mon, 04-Oct-2021
by Shudong Wang; Lisheng Wang; Lei Wang; Zhiyong Yu; Xiumin Zhao; Xun Wang
International Journal of Adaptive and Innovative Systems (IJAIS), Vol. 3, No. 1, 2021
Abstract: Ovarian cancer is one of the most common cancers in female genital organs. Since ovarian cancer is associated with plenty of pathological features, most of the computational diagnostic methods can classify the type of ovarian cancer, but cannot accurately detect the locations. In this work, we propose a novel deep learning model architecture, named AT-Cascade R-CNN. In our model, attention mechanism is firstly involved into Cascade R-CNN. We use attention-based activation function in the backbone network to achieve nonlinear activation. Data experiments are conducted on dataset collected from the affiliated hospital of Qingdao University. After training, our model performs better than baseline Cascade R-CNN and Cascade R-CNN+CBAM in detecting the lesions. The MAP of our model reaches 93.2%, which is better than some state-of-the-art deep learning models. It realises accurate detection of ovarian cancer lesions for the first time, and accurately classifies the lesions.
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