2019/03/17 by Morteza Babaie, Babaie, Morteza, Hamid R. Tizhoosh +2 · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial intelligence #Cell Image Analysis Techniques #Classifier (UML) #Computer science #Computer vision #Convolutional neural network #Digital Imaging for Blood Diseases #Digital pathology #Digitization #Feature (linguistics) #Feature vector #H&E stain #Immunohistochemistry #Magnification #Medicine #Pathology #Pattern recognition (psychology) #Support vector machine #cs.CV #eess.IV
paper · pdf · doi:10.48550/arxiv.1903.07011
published in arXiv (Cornell University) (Cornell University) · Accepted for publication in the 15th European Congress on Digital Pathology (ECDP 2019), University of Warwick, UK
arxiv created 2019/03/17 · openalex publication_date 2019/03/17 · arxiv updated 2019/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope. The formation of tissue folds occur during tissue processing. Their presence may not only cause out-of-focus digitization but can also negatively affect the diagnosis in some cases. In this paper, we have compared five pre-trained convolutional neural networks (CNNs) of different depths as feature extractors to characterize tissue folds. We have also explored common classifiers to discriminate folded tissue against the normal tissue in hematoxylin and eosin (H&E) stained biopsy samples. In our experiments, we manually select the folded area in roughly 2.5mm × 2.5mm patches at 20x magnification level as the training data. The ``DenseNet'' with 201 layers alongside an SVM classifier outperformed all other configurations. Based on the leave-one-out validation strategy, we achieved 96.3% accuracy, whereas with augmentation the accuracy increased to 97.2%. We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was 81%. One folded patch within an image suffices to flag the entire specimen for visual inspection.