ArticlePloS one2017
Grading of invasive breast carcinoma through Grassmannian VLAD encoding.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Synergistic transfer learning and adversarial networks for breast cancer diagnosis: benign vs. invasive classification.Scientific reports · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.Scientific reports · 2023Article
- Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.Journal of cancer research and clinical oncology · 2023Review
- A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.Journal of cancer research and clinical oncology · 2023Article
- Tertiary lymphoid structures in pancreatic cancer: a new target for immunotherapy.Frontiers in immunology · 2023Review
- Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images.Scientific reports · 2022Article
- Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.Scientific reports · 2022Article
- Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets.Sensors (Basel, Switzerland) · 2022Article
- Article
- Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images.Sensors (Basel, Switzerland) · 2022Article
- Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.Computational intelligence and neuroscience · 2022Article
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade.Current oncology (Toronto, Ont.) · 2021Article
- Deep Features for Training Support Vector Machines.Journal of imaging · 2021Article
- 3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images.Entropy (Basel, Switzerland) · 2021Article
- Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images.PeerJ. Computer science · 2021Article
- Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.PloS one · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we address the problem of automated grading of invasive breast carcinoma through the encoding of histological images as VLAD (Vector of Locally Aggregated Descriptors) representations on the Grassmann manifold. The proposed method considers each image as a set of multidimensional spatially-evolving signals that can be efficiently modeled through a higher-order linear dynamical systems analysis. Subsequently, each H&E (Hematoxylin and Eosin) stained breast cancer histological image is represented as a cloud of points on the Grassmann manifold, while a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. To evaluate the efficiency of the proposed methodology, two datasets with different characteristics were used. More specifically, we created a new medium-sized dataset consisting of 300 annotated images (collected from 21 patients) of grades 1, 2 and 3, while we also provide experimental results using a large dataset, namely BreaKHis, containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results have shown that the proposed method outperforms a number of state of the art approaches providing average classification rates of 95.8% and 91.38% with our dataset and the BreaKHis dataset, respectively.
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