Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data.
Zafran Hussain ShahMarcel MüllerWolfgang HübnerTung-Cheng WangDaniel TelmanThomas HuserWolfram SchenckPublished in: GigaScience (2024)
The SwinT-fairSIM method is well suited for denoising SR-SIM images. By fine-tuning, already trained models can be easily adapted to different noise characteristics and cell structures. Furthermore, the provided datasets are structured in a way that the research community can readily use them for research on denoising, super-resolution, and transfer learning strategies.
Keyphrases
- convolutional neural network
- air pollution
- deep learning
- healthcare
- high resolution
- optical coherence tomography
- single cell
- mental health
- cell therapy
- single molecule
- electronic health record
- high throughput
- big data
- high speed
- stem cells
- machine learning
- resistance training
- electron transfer
- label free
- high intensity