Table 1.

Comparisons of the top-performing solutions of the Kaggle Data Science Bowl.

Rank	Team name	Additional datasets used	Data preprocessing	Nodule segmentation	Classification algorithms	Implementation	Final test set score
1	Grt123	LUNA16a	Lung segmentation, intensity normalization	Variant of U-Net	Neural network with a max-pooling layer and two fully connected layers	Pytorch	0.39975
2	Julian de Wit and Daniel Hammack	LUNA16, LIDCb	Rescale to 1×1×1	C3Dc, ResNet-like CNNd	C3D, ResNet-like CNN	Keras, Tensorflow, Theano	0.40117
3	Aidence	LUNA16	Rescale to 2.5×0.512×0.512 (for nodule detection) and 1.25×0.5×0.5 (for classification)	ResNete	3D DenseNetf multitask model (different loss functions depending on the input source)	Tensorflow	0.40127
4	qfpxfd	LUNA16, SPIE-AAPMg	Lung segmentation	Faster R-CNNh, with 3D CNN for false positive reduction	3D CNN inspired by VGGNet	Keras, Tensorflow, Caffe	0.40183
5	Pierre Fillard (Therapixel)	LUNA16	Rescale to 0.625×0.625×0.625, lung segmentation	3D CNN inspired by VGGNet	3D CNN inspired by VGGNet	Tensorflow	0.40409
6	MDai	None	Rescale to 1×1×1, normalize HUi	2D and 3D ResNet	3D ResNet + a Xgboost classifier incorporating CNN output, patient sex, # nodules, and other nodule features	Keras, Tensorflow, Xgboost	0.41629
7	DL Munich	LUNA16	Rescale to 1×1×1, lung segmentation	U-Net	2D and 3D residual neural network	Tensorflow	0.42751
8	Alex, Andre, Gilberto, and Shize	LUNA16	Rescale to 2×2×2	Variant of U-Net	CNN, tree-based classifiers (with better performance)	Keras, Theano, xgboost, extraTree	0.43019
9	Deep Breath	LUNA16, SPIE-AAPMj	Lung mask	Variant of SegNet	Inception-ResNet v2	Theano and Lasagne	0.43872
10	Owkin Team	LUNA16	Lung segmentation	U-Net, 3D VGGNet	Gradient boosting	Keras, Tensorflow, xgboost	0.44068

^aLUNA16: Lung Nodule Analysis 2016.

^bLIDC: Lung Image Database Consortium.

^cC3D: convolutional 3D.

^dResNet-like CNN: residual net–like convolutional neural network.

^eResNet: residual net.

^fDenseNet: dense convolutional network.

^gSPIE-AAPM: International Society for Optics and Photonics–American Association of Physicists in Medicine Lung CT Challenge.

^hR-CNN: region-based convolutional neural networks.

ⁱHU: Hounsfield unit.

^jDataset has been evaluated but not used in building the final model.