Table 1.

A relative shortage of diseased artery/branch APV representations was demonstrated when a 3:1:1 image-dataset distribution was used for training:validation:testing, leading to an undesirable imbalance for training and testing (1A). When 1:1 diseased:normal dataset balancing was applied for training and validation, low case volumes (e.g., only 142:142) was expected to limit training performance (1B). To increase diseased artery/branch representations for training, while maintaining diseased:normal dataset balance, a novel DA method was developed for dataset enlargement through creation of additional depictions of the same arteries/branches (i.e., MPV); by this “mosaicking” DA method alone, the 142 original diseased APVs identified for training were amplified to 710 diseased MPV representations, approximating the 657 non-permuted normal MPVs (1C)

1A	3:1:1 Distributed
	Training	Validation	Testing	Total
Diseased APV	142	50	53	245
Normal APV	657	225	245	1127
1B	3:1:1 Distributed+1:1 balanced
	Training	Validation	Testing	Total
Diseased APV	142	50	53	245
Normal APV	142	50	245	437
1C	3:1:1 Distributed+1:1 balanced+data-augmented for training
	Training	Validation	Testing	Total
Diseased MPV	710	50	53	813
Normal MPV	657	50	245	952