ArticleHealthcare (Basel, Switzerland)2022
A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system.Heliyon · 2024Article
- Intelligent cell images segmentation system: based on SDN and moving transformer.Scientific reports · 2024Article
- Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Review
- A semantic fidelity interpretable-assisted decision model for lung nodule classification.International journal of computer assisted radiology and surgery · 2024Article
- Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images.Bioengineering (Basel, Switzerland) · 2023Article
- An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings.Biomedicines · 2023Article
- Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma.Healthcare (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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Abstract
Histopathological examination is an important criterion in the clinical diagnosis of osteosarcoma. With the improvement of hardware technology and computing power, pathological image analysis systems based on artificial intelligence have been widely used. However, classifying numerous intricate pathology images by hand is a tiresome task for pathologists. The lack of labeling data makes the system costly and difficult to build. This study constructs a classification assistance system (OHIcsA) based on active learning (AL) and a generative adversarial network (GAN). The system initially uses a small, labeled training set to train the classifier. Then, the most informative samples from the unlabeled images are selected for expert annotation. To retrain the network, the final chosen images are added to the initial labeled dataset. Experiments on real datasets show that our proposed method achieves high classification performance with an AUC value of 0.995 and an accuracy value of 0.989 using a small amount of labeled data. It reduces the cost of building a medical system. Clinical diagnosis can be aided by the system's findings, which can also increase the effectiveness and verifiable accuracy of doctors.
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