arXiv:2206.11520
공식 코드 · Papers with Code
ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?
원문 초록
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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Unsupervised cell segmentation by fast Gaussian Processes
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COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
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