ArticleCancers2022
Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 36 citations in OpenAlex.
- External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.European radiology · 2026Article
- Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis.BMC cancer · 2025Article
- AutoRadAI: a versatile artificial intelligence framework validated for detecting extracapsular extension in prostate cancer.Biology methods & protocols · 2025Article
- The analysis of art design under improved convolutional neural network based on the Internet of Things technology.Scientific reports · 2024Article
- Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.European journal of radiology · 2024Article
- Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer.American journal of clinical and experimental urology · 2024Review
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Authors and funding
7 authors at 5 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection.
methodsA multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset.
resultsFour benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively.
conclusionsThe performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.
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