ArticleBMC medical imaging2013
Histological image classification using biologically interpretable shape-based features.
Article in BMC medical imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- From unlabeled to labeled: self-supervised deep learning in computational pathology.BioData mining · 2026Article
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- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- Quantile Index Biomarkers Based on Single-Cell Expression Data.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.Statistica Sinica · 2023Article
- On The Potential of Image Moments for Medical Diagnosis.Journal of imaging · 2023Article
- DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
- A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.Scientific reports · 2022Article
- Quantification of spatial tumor heterogeneity in immunohistochemistry staining images.Bioinformatics (Oxford, England) · 2021Article
- Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification.Journal of imaging · 2021Article
- Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.Medical image analysis · 2021Article
- Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma.PLoS computational biology · 2020Article
- Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment.PloS one · 2019Article
- Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.Computers in biology and medicine · 2018Article
- Predicting cancer outcomes from histology and genomics using convolutional networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis.Journal of biophotonics · 2018Article
- Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies.JCO clinical cancer informatics · 2018Review
- Interactive phenotyping of large-scale histology imaging data with HistomicsML.Scientific reports · 2017Article
- PathoSpotter-K: A computational tool for the automatic identification of glomerular lesions in histological images of kidneys.Scientific reports · 2017Article
- Predicting Heart Rejection Using Histopathological Whole-Slide Imaging and Deep Neural Network with Dropout.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2017Article
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4 authors.
Funding
Abstract
backgroundAutomatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.
methodsWe examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.
resultsThe shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.
conclusionsShape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.
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