ArticleNature communications2024
Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Electrically switchable dual-focus metalens for depth-extended in vivo photoacoustic imaging of ophthalmic vascular disease.Light, science & applications · 2026Article
- A Label-Free Ultraviolet Photoacoustic Microscopy Enables Nuclear Imaging and Toxicity Assessment in an Intact Brain Organoid.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Activatable smart contrast agents for photoacoustic imaging.Smart molecules : open access · 2026Review
- Fabrication of optically transparent ultrasound transducers to integrate light and sound in multimodal biomedical systems.Nature protocols · 2026Review
- HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multi-Task Translation.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.BMC emergency medicine · 2026Article
- Volumetric photoacoustic/ultrasound image fusion and enhancement with dual-tree complex wavelet transform.Biomedical optics express · 2026Article
- Photoacoustic computed tomography monitors cerebrospinal fluid dynamics and glymphatic function.Nature communications · 2026Article
- Photoacoustic Microscopy for Multiscale Biological System Visualization and Clinical Translation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- From light to sound: Seeing and hearing the placenta in health and disease.Science advances · 2026Review
- Label-free mid-infrared dichroism-sensitive photoacoustic microscopy for histostructural analysis of engineered heart tissues.Light, science & applications · 2026Article
- A handheld photoacoustic microscopic probe integrating a transparent ultrasound transducer and a fiber scanner.Nature communications · 2025Article
- Label-free photoacoustic imaging of glassfrog development.Photoacoustics · 2025Article
- A three-dimensional photoacoustic and ultrasound automated breast volume scanner (PAUS-ABVS) for breast cancer patients.Science advances · 2025Article
- Real-Time Robust 2.5D Stereo Multi-Object Tracking with Lightweight Stereo Matching Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Clinical translation of photoacoustic imaging using exogenous molecular contrast agents [Invited].Biomedical optics express · 2025Review
- Multi-photon, label-free photoacoustic and optical imaging of NADH in brain cells.Light, science & applications · 2025Article
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
- Photoacoustic imaging detects cerebrovascular pathological changes in sepsis.Photoacoustics · 2025Article
- Ultrasound-Mediated Membrane Modulation for Biomedical Applications.Nanomaterials (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional confocal fluorescence microscopy. Here, we demonstrate an explainable deep learning-based unsupervised inter-domain transformation of low-resolution unlabeled mid-infrared photoacoustic microscopy images into confocal-like virtually fluorescence-stained high-resolution images. The explainable deep learning-based framework is proposed for this transformation, wherein an unsupervised generative adversarial network is primarily employed and then a saliency constraint is added for better explainability. We validate the performance of explainable deep learning-based mid-infrared photoacoustic microscopy by identifying cell nuclei and filamentous actins in cultured human cardiac fibroblasts and matching them with the corresponding CFM images. The XDL ensures similar saliency between the two domains, making the transformation process more stable and more reliable than existing networks. Our XDL-MIR-PAM enables label-free high-resolution duplexed cellular imaging, which can significantly benefit many research avenues in cell biology.
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