Resolution enhancement of placenta histological images using deep learning
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Abstract (arXiv)
In this study, a method has been developed to improve the resolution of histological human placenta images. For this purpose, a paired series of high- and low-resolution images have been collected to train a deep neural network model that can predict image residuals required to improve the resolution of the input images. A modified version of the U-net neural network model has been tailored to find the relationship between the low resolution and residual images. After training for 900 epochs on an augmented dataset of 1000 images, the relative mean squared error of 0.003 is achieved for the prediction of 320 test images. The proposed method has not only improved the contrast of the low-resolution images at the edges of cells but added critical details and textures that mimic high-resolution images of placenta villous space.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 30 Jul 2022.
Builds on
5 of 11 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
- 2020The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabelingcited by 64
- 2018Towards Deep Cellular Phenotyping in Placental Histologycited by 2
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Citing papers per year: 2022: 1.
1 paper.