ArticleBMC bioinformatics2019
Reverse active learning based atrous DenseNet for pathological image classification.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- Eyebirds: Enabling the Public to Recognize Water Birds at Hand.Animals : an open access journal from MDPI · 2022Article
- E-Commerce Picture Text Recognition Information System Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
- Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images.Multimedia tools and applications · 2022Article
- Design of lung nodules segmentation and recognition algorithm based on deep learning.BMC bioinformatics · 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
- Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- A Transfer Learning-Based Active Learning Framework for Brain Tumor Classification.Frontiers in artificial intelligence · 2021Article
- AI-assisted rapid on-site evaluation ROSE of EUS-FNA cytopathology for pancreatic solid lesions: A two-stage deep learning approach.Endoscopic ultrasoundArticle
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4 authors.
Funding
Abstract
backgroundDue to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack of annotation capabilities. To address these challenges, we propose a training strategy called deep-reverse active learning (DRAL) and atrous DenseNet (ADN) for pathological image classification. The proposed DRAL can improve the classification accuracy of widely used deep learning networks such as VGG-16 and ResNet by removing mislabeled patches in the training set. As the size of a cancer area varies widely in pathological images, the proposed ADN integrates the atrous convolutions with the dense block for multiscale feature extraction.
resultsThe proposed DRAL and ADN are evaluated using the following three pathological datasets: BACH, CCG, and UCSB. The experiment results demonstrate the excellent performance of the proposed DRAL + ADN framework, achieving patch-level average classification accuracies (ACA) of 94.10%, 92.05% and 97.63% on the BACH, CCG, and UCSB validation sets, respectively.
conclusionsThe DRAL + ADN framework is a potential candidate for boosting the performance of deep learning models for partially mislabeled training datasets.
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