Multimodal Fusion Classification Network Based on Distance Confidence Score

doi:10.16183/j.cnki.jsjtu.2020.186

Abstract

Abstract:

Multimodal data modeling can effectively overcome the problem of insufficient information in a single mode and can greatly improve the performance of model. However, not much progress has been made in quantifying the confidence of neural network models, especially for multimodal fusion models. This paper proposes a method based on embedding, which calculates the local density estimation in the embedding space by calculating the distance between samples, and then calculates the confidence score of the model. The proposed method is scalable and can be used not only for a single modal model, but also for the confidence measurement of multimodal fusion model. In addition, it can also evaluate and quantify the influences of different modal data on the multimodal fusion model.

Key words: confidence, multimodal fusion, neural network

CLC Number:

TP181

ZHENG Dezhong, YANG Yuanyuan, HUANG Haozhe, XIE Zhe, LI Wentao. Multimodal Fusion Classification Network Based on Distance Confidence Score[J]. Journal of Shanghai Jiao Tong University, 2022, 56(1): 89-100.

Figures/Tables 21

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Multimodal data based on distance confidence score

数据组合	$δ DCS mode$ ×10²	$δ DCS ima$ × 10²	$δ DCS clin$ × 10²	$δ DCS chec$ × 10²
图像+临床信息	87.572	88.145	70.000	-
图像+血液检查	87.970	88.423	-	69.733
图像+临床信息+血液检查	88.070	88.570	68.630	68.942

Tab.7

References 30

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相关性	R
(MMCS, ECS)	-0.998
(MMCS, DCS)	0.979
(ECS, DCS)	-0.988

损失函数	A/%	AUC×10²	BS_o×10³	BS_I×10³
交叉熵损失	98.34	99.964	30.184	27.182
中心损失	98.48	99.964	25.082	24.419

相关性	R
(MMCS, ECS)	-0.999
(MMCS, DCS)	0.998
(ECS, DCS)	-0.998

损失函数	A/%	AUC×10²	BS_o×10³	BS_I×10³
交叉熵损失	71.53	93.468	49.570	46.878
中心损失	71.65	94.769	45.492	43.161

相关性	R
(MMCS, ECS)	-0.984
(MMCS, DCS)	0.993
(ECS, DCS)	-0.967