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Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks
2016
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. For HC-SVM we further observe that training a second-level window classifier
doi:10.1109/isbi.2016.7493530
dblp:conf/isbi/LiMAZTM16
fatcat:2cnohhllqngopog7fwpstek6nq