Table 3. Results for weak augmentation.

Comparison of HiDisc with the SimCLR and SupCon on SRH and TCGA datasets with weak augmentation (random flipping). Both SimCLR and HiDisc-Patch patch collapse since weak augmentation will make the pretext task trivial. SupCon, on the other hand, is not sensitive to data augmentations since its positive pairs are defined by class labels. HiDisc-Slide and HiDisc-Patient are only slightly affected by the augmentation and achieve performance close to the supervised method.

	SRH - Patch		SRH - Patient		TCGA - Patch		TCGA - Patient
	Accuracy	MCA	Accuracy	MCA	Accuracy	AUROC	Accuracy	AUROC
SimCLR [9]	31.5 (2.3)	23.1 (1.9)	40.2 (6.9)	28.9 (4.5)	57.1 (1.1)	58.4 (2.2)	58.1 (1.1)	72.8 (3.2)
HiDisc-Patch	31.3 (0.6)	22.2 (0.5)	47.4 (2.1)	33.1 (1.6)	59.0 (0.8)	61.5 (1.3)	61.2 (4.2)	75.8 (2.5)
HiDisc-Slide	82.8 (0.2)	77.4 (0.3)	84.2 (0.5)	82.3 (0.4)	79.6 (0.1)	86.3 (0.2)	77.7 (0.4)	85.3 (0.3)
HiDisc-Patient	84.9 (0.2)	78.9 (0.1)	84.7 (0.5)	80.9 (1.4)	82.9 (0.2)	89.6 (0.2)	82.3 (0.3)	90.3 (0.3)
SupCon [27]	90.0 (0.2)	87.4 (0.3)	90.0 (0.5)	90.3 (0.4)	85.4 (0.4)	92.0 (0.2)	88.4 (0.8)	95.2 (0.4)