Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OOD detection algorithm, SimCLR-LOF, as well as a recent state of the art approach (SSD), applied on medical images. SimCLR-LOF learns semantically meaningful features using SimCLR and uses LOF for scoring if a test sample is OOD. We evaluated on the multi-source International Skin Imaging Collaboration (ISIC) 2019 dataset, and show results that are competitive with SSD as well as with recent supervised approaches applied on the same data.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
ColorJitter •
Convolution •
Dense Connections •
Feedforward Network •
Global Average Pooling •
Kaiming Initialization •
Max Pooling •
Non Maximum Suppression •
NT-Xent •
Random Gaussian Blur •
Random Resized Crop •
ReLU •
Residual Block •
Residual Connection •
ResNet •
SimCLR •
SSD
