ArticleMedical image analysis2015
High-throughput histopathological image analysis via robust cell segmentation and hashing.
Article in Medical image analysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed, 90 citations in OpenAlex.
- Article
- SenseCare: a research platform for medical image informatics and interactive 3D visualization.Frontiers in radiology · 2024Article
- Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning.BMC medical imaging · 2023Article
- Article
- Article
- Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma.Frontiers in cell and developmental biology · 2021Article
- AI in Medical Imaging Informatics: Current Challenges and Future Directions.IEEE journal of biomedical and health informatics · 2020Article
- Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma.Frontiers in cell and developmental biology · 2020Article
- Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images.Frontiers in neuroanatomy · 2019Article
- Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.Neuroinformatics · 2018Article
- Identification of topological features in renal tumor microenvironment associated with patient survival.Bioinformatics (Oxford, England) · 2018Article
- I-AbACUS: a Reliable Software Tool for the Semi-Automatic Analysis of Invasion and Migration Transwell Assays.Scientific reports · 2018Article
- Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis.Cancer research · 2017Article
- SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.PloS one · 2017Article
- Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images.Scientific reports · 2016Article
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.IEEE reviews in biomedical engineering · 2016Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.