Table 3:

Statistical Results of Different Models on Different Metrics with Data Split 2. (a) Train classification model with raw CT data. (b) Train classification model with data masked by ground-truth segmentation. (c) Train classification model with data masked by automatically generated segmentation map (ours1). (d) Jointly train classification model and segmentation model(ours2). (e) Multi-task learning model with segmentation and classification.

	Accuracy(%)					AveF1(%)
	(a)	(b)	(c)(ours1)	(d)(ours2)	(e)	(a)	(b)	(c)(ours1)	(d)(ours2)	(e)
1)	54.67±0.31	57.78±0.36	56.00±0.25	54.67±0.21	52.67±0.41	52.90±0.31	57.74±0.37	54.40±0.21	53.79±0.21	51.17±0.41
2)	72.00±0.33	74.67±0.36	72.89±0.29	73.78±0.21	72.56±0.31	70.31±0.31	73.86±0.25	71.00±0.21	71.48±0.23	70.75±0.46
3)	82.67±0.31	84.00±0.34	83.11±0.36	83.11±0.36	82.22±0.41	81.20±0.33	82.67±0.33	81.98±0.24	81.70±0.24	82.10±0.36
4)	64.78±0.41	71.07±0.33	67.92±0.29	67.29±0.35	65.41±0.35	64.33±0.31	69.10±0.32	64.69±0.27	66.33±0.29	64.94±0.34
	MCC					AUC
	(a)	(b)	(c)(ours1)	(d)(ours2)	(e)	(a)	(b)	(c)(ours1)	(d)(ours2)	(e)
1)	2.697±0.056	3.418±0.049	2.743±0.047	2.750±0.041	2.731±0.057	-	-	-	-	-
2)	3.204±0.	4.033±0.031	3.232±0.029	3.210±0.026	3.217±0.037	7.052±0.036	7.469±0.035	7.157±0.031	7.154±0.025	7.057±0.041
3)	4.819±0.033	5.263±0.033	5.018±0.029	4.978±0.024	4.821±0.031	7.921±0.043	8.106±0.044	7.942±0.047	8.008±0.041	7.934±0.055
4)	2.351±0.031	3.393±0.030	2.530±0.029	2.657±0.027	2.281±0.031	6.072±0.025	6.882±0.025	6.591±0.021	6.567±0.027	6.148±0.037