Table 2.

Cross-validation results (out of fold): Cohen’s kappa coefficients for each of the trained tasks on out-of-fold sample (OAI dataset). The best results task-wise are highlighted in bold. We selected two best models for thorough evaluation: SE-Resnet-50 ${}^{†}$ and SE-ResNext50-32x4d ${}^{‡}$. We trained these models from scratch (*) and also with transfer learning, but without the KL-grade (**). Finally, in the last row, we show the results for the ensembling of these models. L and M indicate lateral and medial compartments, respectively. FO and TO indicate femoral and tibial osteophytes and JSN indicates joint space narrowing, respectively. KL indicates the Kellgren–Lawrence grade.

Backbone	KL	FO		TO		JSN
		L	M	L	M	L	M
Resnet-18	0.81	0.71	0.78	0.80	0.76	0.91	0.87
Resnet-34	0.81	0.69	0.78	0.80	0.76	0.90	0.87
Resnet-50	0.81	0.70	0.78	0.81	0.78	0.91	0.87
SE-Resnet-50 ${}^{†}$	0.81	0.71	0.79	0.81	0.78	0.91	0.87
SE-ResNext50-32x4d ${}^{‡}$	0.81	0.72	0.79	0.82	0.78	0.91	0.87
SE-Resnet-50 *	0.78	0.66	0.73	0.76	0.70	0.91	0.87
SE-ResNext50-32x4d *	0.77	0.67	0.73	0.75	0.71	0.91	0.87
SE-Resnet-50 **	-	0.71	0.79	0.82	0.78	0.91	0.88
SE-ResNext50-32x4d **	-	0.73	0.80	0.83	0.78	0.91	0.88
Ensemble ${}^{†,‡}$	0.82	0.73	0.80	0.83	0.79	0.92	0.88