ArticlePattern recognition2019
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.
Article in Pattern recognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
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- Small Object Localization with 90% Annotation Reduction by Positive-Unlabeled Learning.Micromachines · 2025Article
- Hybrid Deep Learning Models for Analyzing Histological Images of the Zebrafish Intestine Under Oxidative Stress.Diagnostics (Basel, Switzerland) · 2025Article
- A Robust Semi-Supervised Brain Tumor MRI Classification Network for Data-Constrained Clinical Environments.Diagnostics (Basel, Switzerland) · 2025Article
- Optimization of deep learning models for inference in low resource environments.Computers in biology and medicine · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Breast Cancer Histopathological Images Segmentation Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Efficient end-to-end learning for cell segmentation with machine generated weak annotations.Communications biology · 2023Article
- Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods.Journal of pathology informatics · 2023Article
- Article
- Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts.Journal of digital imaging · 2022Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network.BMC medical informatics and decision making · 2022Article
- Construction of Home Product Design System Based on Self-Encoder Depth Neural Network.Computational intelligence and neuroscience · 2022Article
- Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus.Frontiers in immunology · 2022Article
- Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.Cancers · 2021Article
- Local Integral Regression Network for Cell Nuclei Detection.Entropy (Basel, Switzerland) · 2021Article
- Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.Nature communications · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully- supervised annotation cost.
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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.