The Gleason grade group (GG) is an important basis for assessing the malignancy of prostate cancer, but it requires invasive biopsy to obtain pathology. To noninvasively evaluate GG, an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning. First, a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area. Second, the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area. Finally, in the network training, a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed, which further improves the prediction performance of the network. Experimental results show that the proposed method is better than the traditional network model in predicting GG performance. The quadratic weighted Kappa is 0.471 1 and the positive predictive value for predicting clinically significant cancer is 0.936 9.
SHEN Ao1,2‡ (沈傲), HU Jisu 2,3‡ (胡冀苏), JIN Pengfei4 (金鹏飞), ZHOU Zhiyong2 (周志勇), QIAN Xusheng 2,3 (钱旭升), ZHENG Yi2 (郑毅), BAO Jie 4 (包婕), WANG Ximing4∗ (王希明), DAI Yakang1,2∗ (戴亚康)
. Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(1)
: 109
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DOI: 10.1007/s12204-022-2502-x
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