CancerDetection

https://www.kaggle.com/c/histopathologic-cancer-detection

Dependencies

For install dependencies. Run

pip3 install -r requirements.txt

Testing

Models are presented here

Solo models

Number of expetiment	Augmentation	Network architecture	Additional params	Accuracy
1	A	ResNet18	Epoch 18	0.9028
2	A	ResNet18	Epoch 1	0.8897
3	A	MobileNetV2	Epoch 1	0.8892
4	A	MobileNetV2	Epoch 6	0.8850
5	A	MobileNet	Epoch 7	0.8839
6	A	ResNet50	Epoch 7	0.8948
7	A	ResNet50	Epoch 17	0.8949
8	B	ResNet18	Epoch 2	0.9016
9	B	ResNet18	Epoch 3	0.9044
10	B	ResNet18	Epoch 6	0.9072
11	B	ResNet18	Epoch 9	0.9080
12	B	ResNet18	Epoch 12	0.8985
13	B	ResNet18	Epoch 22	0.8878

Ensembling

Number of expetiment	Threshold	Number network from prev. table	Accuracy
1	Without thr	1, 3, 6 - 12	0.9515
1	> 0.8 = 1	1, 3, 6 - 12	0.9510
1	> 0.5 = 1	1, 3, 6 - 12	0.9484

Types of augmentation

A

# pretrained ImageNet network
T.Resize((224,224))
T.ColorJitter(brightness=0.5, contrast=0.5),
T.RandomRotation((0, 5)),
T.Normalize(mean, std) # ImageNet

B

# pretrained ImageNet network
T.Resize((224,224))
T.ColorJitter(brightness=0.5, contrast=0.5),
T.RandomRotation((-90, 90)),
T.RandomHorizontalFlip(p=0.5),
T.RandomVerticalFlip(p=0.5)
T.Normalize(mean, std) # ImageNet