Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.
Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.
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卷积神经网络（CNN）在识别口腔癌方面表现出了良好的前景。然而，缺乏可解释性和可靠性仍然是开发值得信赖的计算机辅助诊断系统的主要挑战。为了解决这个问题，我们提出了一种集成视觉解释和注意力机制的神经网络架构。它通过注意力机制提高识别性能，同时为决策提供可解释性。此外，我们的系统采用了人机循环（HITL）深度学习，通过人机智能的集成来提高系统的可靠性和准确性。我们通过手动编辑注意力机制的注意力图，将专家知识嵌入到网络中。卷积神经网络在口腔癌检测和分类方面表现出了优异的性能。然而，端到端的学习策略使得 CNN 难以解释，并且完全理解决策过程可能具有挑战性。此外，可靠性也是基于 CNN 的方法的一个重大挑战。在本研究中，我们提出了一种称为注意分支网络（ABN）的神经网络，它将视觉解释和注意机制结合起来，以提高识别性能并同时解释决策。我们还通过让人类专家手动编辑注意力机制的注意力图，将专家知识嵌入到网络中。我们的实验表明，ABN 的性能优于原始基线网络。通过向网络引入挤压和激励（SE）块，交叉验证的准确性进一步提高。此外，我们观察到，通过手动编辑注意力图进行更新后，一些先前错误分类的案例被正确识别。使用 ABN（Resnet18 作为基线）时，交叉验证精度从 0.846 提高到 0.875，使用 SE-ABN 时，交叉验证精度从 0.877 提高到 0.877，嵌入专家知识后，交叉验证精度提高到 0.903。该方法通过视觉解释、注意力机制和专家知识嵌入，提供了准确、可解释、可靠的口腔癌计算机辅助诊断系统。
Convolutional neural networks (CNNs) have shown promising performance in recognizing oral cancer. However, the lack of interpretability and reliability remain major challenges in the development of trustworthy computer-aided diagnosis systems. To address this issue, we proposed a neural network architecture that integrates visual explanation and attention mechanisms. It improves the recognition performance via the attention mechanism while simultaneously providing interpretability for decision-making. Furthermore, our system incorporates Human-in-the-loop (HITL) deep learning to enhance the reliability and accuracy of the system through the integration of human and machine intelligence. We embedded expert knowledge into the network by manually editing the attention map for the attention mechanism. Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.
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影响因子：
15.2
作者：
Xue P;Wang J;Qin D;Yan H;Qu Y;Seery S;Jiang Y;Qiao Y
通讯作者：
Qiao Y
影响因子：
82.9
作者：
Lotter W;Diab AR;Haslam B;Kim JG;Grisot G;Wu E;Wu K;Onieva JO;Boyer Y;Boxerman JL;Wang M;Bandler M;Vijayaraghavan GR;Gregory Sorensen A
通讯作者：
Gregory Sorensen A
影响因子：
3.7
作者：
Uthoff, Ross D.;Song, Bofan;Liang, Rongguang
通讯作者：
Liang, Rongguang
影响因子：
3.5
作者：
Song B;Sunny S;Li S;Gurushanth K;Mendonca P;Mukhia N;Patrick S;Gurudath S;Raghavan S;Imchen T;Leivon S;Kolur T;Shetty V;Bushan V;Ramesh R;Lima N;Pillai V;Wilder-Smith P;Sigamani A;Suresh A;Kuriakose M;Birur P;Liang R
通讯作者：
Liang R
影响因子：
4.6
作者：
Birur, Praveen N.;Song, Bofan;Sunny, Sumsum P.;Keerthi, G.;Mendonca, Pramila;Mukhia, Nirza;Li, Shaobai;Patrick, Sanjana;Shubha, G.;Subhashini, A. R.;Imchen, Tsusennaro;Leivon, Shirley T.;Kolur, Trupti;Shetty, Vivek;Bhushan, Vidya R.;Vaibhavi, Daksha;Rajeev, Surya;Pednekar, Sneha;Banik, Ankita Dutta;Ramesh, Rohan Michael;Pillai, Vijay;Kathryn, O. S.;Smith, Petra Wilder;Sigamani, Alben;Suresh, Amritha;Liang, Rongguang;Kuriakose, Moni A.
通讯作者：
Kuriakose, Moni A.