Pretrain on SCI lesion segmentation then finetune on DCM lesions

[naga-karthik]

With SCIseg, we noticed that the model was also decently segmenting DCM lesions, which strongly suggested that it simply learned to be sensitive to T2w hyperintensities in the images. We could use this to our advantage for DCM lesion segmenation.

Instead of training from scratch on a small dataset (~50 subjects), we could pretrain on SCI data from all the 3 sites and then fine-tune on DCM lesions. My hypothesis is that pretraining should result in some boost in the model performance for DCM lesion seg.

[naga-karthik]

The first results for pre-training and fine-tuning (both region-based) are in!

Fine-tuning performance on `dcm-zurich-lesions`

Test Phase Metrics [ANIMA] for sc: 
	Jaccard --> Mean: 0.944, Std: 0.007
	Dice --> Mean: 0.971, Std: 0.004
	Sensitivity --> Mean: 0.971, Std: 0.003
	Specificity --> Mean: 1.000, Std: 0.000
	PPV --> Mean: 0.971, Std: 0.005
	NPV --> Mean: 1.000, Std: 0.000
	RelativeVolumeError --> Mean: 0.051, Std: 0.499
	HausdorffDistance --> Mean: 1.414, Std: 0.000
	ContourMeanDistance --> Mean: 0.204, Std: 0.039
	SurfaceDistance --> Mean: 0.001, Std: 0.001

Test Phase Metrics [ANIMA] for lesion: 
	Jaccard --> Mean: 0.432, Std: 0.328
	Dice --> Mean: 0.517, Std: 0.377
	Sensitivity --> Mean: 0.502, Std: 0.361
	Specificity --> Mean: 1.000, Std: 0.000
	PPV --> Mean: 0.535, Std: 0.395
	NPV --> Mean: 1.000, Std: 0.000
	RelativeVolumeError --> Mean: -28.241, Std: 32.595
	HausdorffDistance --> Mean: 5.333, Std: 6.128
	ContourMeanDistance --> Mean: 3.304, Std: 4.316
	SurfaceDistance --> Mean: 1.895, Std: 2.646
	PPVL --> Mean: 0.667, Std: 0.471
	SensL --> Mean: 0.500, Std: 0.408
	F1_score --> Mean: 0.556, Std: 0.416

Fine-tuning performance on `dcm-zurich-lesions-20231115`

Test Phase Metrics [ANIMA] for sc: 
	Jaccard --> Mean: 0.933, Std: 0.023
	Dice --> Mean: 0.965, Std: 0.012
	Sensitivity --> Mean: 0.958, Std: 0.024
	Specificity --> Mean: 1.000, Std: 0.000
	PPV --> Mean: 0.973, Std: 0.010
	NPV --> Mean: 1.000, Std: 0.000
	RelativeVolumeError --> Mean: -1.500, Std: 2.833
	HausdorffDistance --> Mean: 1.559, Std: 0.565
	ContourMeanDistance --> Mean: 0.325, Std: 0.089
	SurfaceDistance --> Mean: 0.001, Std: 0.001

Test Phase Metrics [ANIMA] for lesion: 
	Jaccard --> Mean: 0.220, Std: 0.186
	Dice --> Mean: 0.322, Std: 0.256
	Sensitivity --> Mean: 0.246, Std: 0.227
	Specificity --> Mean: 1.000, Std: 0.000
	PPV --> Mean: 0.783, Std: 0.340
	NPV --> Mean: 1.000, Std: 0.000
	RelativeVolumeError --> Mean: -67.966, Std: 31.173
	HausdorffDistance --> Mean: 6.302, Std: 3.874
	ContourMeanDistance --> Mean: 2.602, Std: 2.090
	SurfaceDistance --> Mean: 0.567, Std: 0.903
	PPVL --> Mean: 0.375, Std: 0.484
	SensL --> Mean: 0.562, Std: 0.464
	F1_score --> Mean: 0.375, Std: 0.484

The performance on dcm-zurich-lesions-20231115 does not seem to be good. Maybe it could be due to region-based training? A multi-channel model might improve the results