Table 3: Performance comparison with state-of-the-art approaches on all datasets

I'm sorry, but without additional context or information, I'm not able to provide a meaningful response. Can you please provide more details about the datasets and the state-of-the-art approaches being compared?

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4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

4 实验
本节将研究所提出的IFCS-MOEA框架的有效性。首先，在第4.1节中介绍实验设置。其次，在第4.2节中研究IFCS对MOEA/D-DE的影响。然后，在第4.3节中，将IFCS-MOEA/D-DE与19个测试问题上的五种最先进的MOEA进行比较。最后，在第4.4节中，将IFCS-MOEA/D-DE与四种真实世界应用问题上的五种最先进的MOEA进行比较。

4.1 实验设置
实验中将MOEA/D-DE [23]与所提出的框架集成，得到的算法称为IFCS-MOEA/D-DE。比较使用了五种基于代理的MOEA，即FCS-MOEA/D-DE [39]，CPS-MOEA [41]，CSEA [29]，MOEA/D-EGO [43]和EDN-ARM-OEA [12]。使用了具有复杂PS的UF1-10、LZ1-9测试问题[44，23]。其中，UF1-7、LZ1-5和LZ7-9具有2个目标，UF8-10和LZ6具有3个目标。UF1-10、LZ1-5和LZ9具有30个决策变量，LZ6-8具有10个决策变量。所有比较算法的种群大小N都设置为45。由于这些问题被视为昂贵的MOP [39]，所以最大FE的数量设置为500。对于每个测试问题，每个算法都独立运行21次。对于IFCS-MOEA/D-DE，将wmax设置为30，将η设置为5。对于其他算法，我们使用其论文中建议的设置。使用IGD [6]指标评估每个算法的性能。所有算法都在PlatEMO [34]平台上进行检验。

The modified CNN architecture was able to provide joint information of multiple modalities for the DLR analysis. The performance of multiple modalities was evaluated using the dataset of the first cohort with both T2 flair images and T1 contrast images. Two evaluation methods were used: leave-one-out cross-validation and validation based on time of diagnosis. The evaluation parameters of the prediction results based on leave-one-out cross-validation are shown in Table 2(b), and a comparison of the ROC curves is presented in Fig. 3(b). 解释

这段话主要说明了一个修改过的卷积神经网络(CNN)架构可以提供多个模态的联合信息进行 DLR(深度学习诊断)分析，并使用第一组数据集中的T2 FLAIR图像和T1对比图像来评估多个模态的性能。作者使用了两种评估方法：留一交叉验证和基于诊断时间的验证。文章给出了留一交叉验证的预测结果的评估参数，表格2(b)中列出了这些参数，同时也提供了 ROC 曲线的比较，如图3(b)所示。