ArticleJournal of pathology informatics2018
Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.
Article in Journal of pathology informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.The American journal of pathology · 2026Article
- ViT-Stain: Vision transformer-driven virtual staining for skin histopathology via global contextual learning.PloS one · 2026Article
- VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology.Scientific reports · 2025Article
- Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images.Bioengineering (Basel, Switzerland) · 2025Article
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.Journal of imaging informatics in medicine · 2024Article
- Artificial Intelligence in pathology: current applications, limitations, and future directions.Irish journal of medical science · 2024Review
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- One label is all you need: Interpretable AI-enhanced histopathology for oncology.Seminars in cancer biology · 2023Review
- Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.Scientific reports · 2023Article
- Article
- Skin Lesion Segmentation in Dermoscopic Images with Noisy Data.Journal of digital imaging · 2023Article
- Refining skin lesions classification performance using geometric features of superpixels.Scientific reports · 2023Article
- Article
- Objective evaluation of chondrocyte density & cloning after joint injury using convolutional neural networks.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022Article
- A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis.Cancers · 2022Article
- NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM.Bioengineering (Basel, Switzerland) · 2022Article
- Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.SLAS discovery : advancing life sciences R & D · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundAdvances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades.
methodsIn this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network.
resultsThe proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques.
conclusionsThe proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.
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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.