Table I.

Target tasks for Transfer Learning. We transfer the learned representations by fine-tuning it for seven medical imaging applications including 3D and 2D image classification and segmentation tasks. To evaluate the generalization ability of our TransVW, we select a diverse set of applications ranging from the tasks on the same dataset as pre-training to the tasks on the unseen organs, datasets, or modalities during pre-training. For each task, ✓denotes the properties that are in common between the pretext and target tasks.

Code*	Application	Object	Modality	Dataset	Common with pretext task
					Organ	Dataset	Modality
NCC	Lung nodule false positive reduction	Lung Nodule	CT	LUNA16 [7]	✓	✓	✓
NCS	Lung nodule segmentation	Lung Nodule	CT	LIDC-IDRI [8]	✓	✓	✓
ECC	Pulmonary embolism false positive reduction	Pulmonary Emboli	CT	PE-CAD [9]	✓		✓
LCS	Liver segmentation	Liver	CT	LiTS-2017 [10]			✓
BMS	Brain Tumor Segmentation	Brain Tumor	MRI	BraTS2018 [11]
DXC	Fourteen thorax diseases classification	Thorax Diseases	X-ray	ChestX-Ray14 [12]	✓	✓	✓
PXS	Pneumothorax Segmentation	Pneumothorax	X-ray	SIIM-ACR-2019 [13]	✓		✓

^*The first letter denotes the object of interest (“N” for lung nodule, “B” for brain tumor, “L” for liver, etc); the second letter denotes the modality (“C” for CT, “X” for X-ray, “M” for MRI); the last letter denotes the task (“C” for classification, “S” for segmentation).