Table 5.

Spearman’s rank correlation coefficient is calculated between all model pairs and is averaged over all five splits.

		Without				With
		OTS	FT	1channel	large	OTS	FT	1channel	large
Without	OTS	—	0.65	0.74	0.73	0.46	0.38	0.40	0.59
	FT	0.65	—	0.81	0.80	0.38	0.42	0.43	0.64
	1channel	0.74	0.81	—	0.93	0.41	0.43	0.47	0.71
	large	0.73	0.80	0.93	—	0.40	0.43	0.47	0.71
With	OTS	0.46	0.38	0.41	0.40	—	0.32	0.33	0.39
	FT	0.38	0.42	0.43	0.43	0.32	—	0.35	0.42
	1channel	0.40	0.43	0.47	0.47	0.33	0.35	—	0.45
	large	0.59	0.64	0.71	0.71	0.39	0.42	0.45	—

Our experiments are grouped into three categories. First, “Without” and “With” non-image features. Second, transfer-learning with off-the-shelf (OTS) and fine-tuned (FT) models. Third, from scratch where “1channel” refers to same input size as in transfer-learning but changed number of channels. “large” means we changed the input dimensions to 448 × 448 × 1. We identify three clusters: all models under “With”, models trained from scratch and “Without”, and the “OTS” model.