Table 2:

Statistical Results of Different Models on Different Metrics with Data Split 1. (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)	55.73±0.34	59.11±0.33	57.78±0.24	58.67±0.27	57.33±0.51	55.19±0.35	58.88±0.38	58.04±0.31	58.30±0.31	56.72±0.46
2)	74.22±0.37	78.22±0.31	76.89±0.25	78.22±0.27	74.33±0.31	74.27±0.31	78.62±0.37	75.69±0.29	77.53±0.25	72.06±0.42
3)	82.22±0.31	83.56±0.35	82.67±0.27	82.67±0.27	81.33±0.45	80.74±0.31	82.27±0.39	81.12±0.25	81.32±0.25	80.74±0.41
4)	67.29±0.51	72.32±0.38	71.70±0.31	72.30±0.30	67.67±0.33	67.29±0.43	72.95±0.35	70.34±0.35	72.22±0.21	65.78±0.49
	MCC(×10−1)					AUC(×10−1)
	(a)	(b)	(c)(ours1)	(d)(ours2)	(e)	(a)	(b)	(c)(ours1)	(d)(ours2)	(e)
1)	3.259±0.057	3.561±0.054	3.415±0.037	3.430±0.035	3.238±0.047	-	-	-	-	-
2)	4.121±0.039	5.128±0.031	4.467±0.041	4.828±0.037	4.239±0.045	7.441±0.031	8.140±0.038	7.943±0.039	8.129±0.031	7.483±0.045
3)	4.692±0.033	5.134±0.031	4.819±0.025	4.852±0.021	4.548±0.038	7.852±0.031	8.044±0.036	7.884±0.039	7.998±0.041	7.802±0.046
4)	3.000±0.039	4.051±0.039	3.931±0.021	4.029±0.021	3.120±0.048	6.557±0.023	7.542±0.035	7.437±0.031	7.412±0.034	6.809±0.048