ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?
Biomarkers identify a patients response to treatment. With the recent advances in artificial intelligence based on the Transformer networks, there is only limited research has been done to measure the performance on challenging histopathology images. In this paper, we investigate the efficacy of the numerous state-of-the-art Transformer networks for immune-checkpoint biomarker, Inducible Tcell COStimulator (ICOS) protein cell segmentation in colon cancer from immunohistochemistry (IHC) slides. Extensive and comprehensive experimental results confirm that MiSSFormer achieved the highest Dice score of 74.85% than the rest evaluated Transformer and Efficient U-Net methods.
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Methods
Absolute Position Encodings •
Adam •
BPE •
Concatenated Skip Connection •
Convolution •
Dense Connections •
Dropout •
Label Smoothing •
Layer Normalization •
Linear Layer •
Max Pooling •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
ReLU •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer •
U-Net
