Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection
Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation. For Semantic segmentation task, we propose a multi-plateau ensemble of FPN (Feature Pyramid Network) with EfficientNet as feature extractor/encoder. For Object detection task, we used a three model ensemble of RetinaNet with Resnet50 Backbone and FasterRCNN (FPN + DC5) with Resnext101 Backbone}. A PyTorch implementation to our approach to the problem is available at https://github.com/ubamba98/EAD2020.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Convolution •
Dense Connections •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
EfficientNet •
Faster R-CNN •
Focal Loss •
FPN •
Global Average Pooling •
Grouped Convolution •
Inverted Residual Block •
Kaiming Initialization •
Pointwise Convolution •
ReLU •
Residual Connection •
ResNeXt •
ResNeXt Block •
RetinaNet •
RMSProp •
RoIPool •
RPN •
Sigmoid Activation •
Softmax •
Squeeze-and-Excitation Block •
Swish
