Cross-modality transformations in biological microscopy enabled by deep learning
Problem
Deep-learning methods can virtually transform microscopy images across optical modalities, but biologists lack a unified overview, limiting adoption in daily research and clinical trials.
Why it matters
Wider adoption of these transformations could enable multimodal imaging analysis previously impossible for biologists and clinicians.
What the AI did
The authors provide a structured review of deep-learning cross-modality transformation methods, covering applications, data sets and implementations for contrast and resolution enhancement.
Results
not stated in source
Impact
Offers a structured resource to help biologists and developers understand and adopt DL cross-modality transformations.
Record
| Venue | Advanced Photonics |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Dana Hassan, Jesús Domínguez, Benjamin Midtvedt, Henrik Klein Moberg, Jesús D. Pineda, Christoph Langhammer, Giovanni Volpe, Antoni Homs-Corbera et al. |
| Citations | 23 (OpenAlex, at import time) |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1117/1.ap.6.6.064001 |
| Open access | diamond |
| Keywords | deep learning, microscopy, cross-modality transformation, contrast enhancement, resolution enhancement, review |
Importance rating
real but narrower advance
Review consolidating DL cross-modality microscopy transformations to boost adoption; enables multimodal imaging without physical staining, a useful but methodological, field-specific advance with no stated scale.