Single Exposure Quantitative Phase Imaging with a Conventional Microscope Using Diffusion Models
Authors: Gabriel della Maggiora, Luis Alberto Croquevielle, Harry Horsley, Thomas Heinis, Artur Yakimovich
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we employ a widespread brightfield microscope equipped with a commercially available color camera. We apply our model to clinical microscopy of patients urine, obtaining accurate phase measurements. To evaluate our model, we conduct three distinct experiments. First, we generate a set of synthetic samples analogous to those in the training set. Second, we acquire four through-focus stacks (which allow us to compute a ground truth) and subsequently test our method on these images. Finally, we use an uncurated clinical dataset comprised of real-world clinical images. Table 1 shows the results, and Figure 2 shows a qualitative comparison of the methods. |
| Researcher Affiliation | Academia | 1 Center for Advanced Systems Understanding (CASUS), G orlitz, Germany 2 Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR), Dresden, Germany 3 Department of Computing, Imperial College London, London, United Kingdom 4 Bladder Infection and Immunity Group (BIIG), UCL Centre for Kidney and Bladder Health, Division of Medicine, University College London, Royal Free Hospital Campus, London, United Kingdom 5 Institute of Computer Science, University of Wrocław, Wrocław, Poland 6 School of Computation, Information and Technology, Technical University of Munich, Germany |
| Pseudocode | No | No specific pseudocode or algorithm blocks are present in the paper. The methodology is described using mathematical equations and textual explanations in Section 3. |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state that the code will be made open-source. No repository links are included. |
| Open Datasets | Yes | To simulate polychromatic acquisitions, we use the Image Net dataset (Deng et al. 2009). To validate the model s performance, we use the HCOCO dataset (Cong et al. 2019) to simulate synthetic examples. We conduct experiments using the Urinary Tract Infection (UTI) dataset as outlined in (Liou et al. 2024). |
| Dataset Splits | No | The paper uses the ImageNet, HCOCO, and Urinary Tract Infection (UTI) datasets but does not provide specific training/validation/test dataset splits, percentages, or methodology for partitioning these datasets. |
| Hardware Specification | No | The paper describes the optical hardware used for image acquisition (e.g., 'Olympus BX41F microscope', 'Infinity 3S-1UR, Teledyne Lumenera camera'). However, it does not provide any specific hardware details such as GPU or CPU models used for training or running the diffusion models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for reproducing the methodology (e.g., programming languages, libraries, or frameworks with their versions). |
| Experiment Setup | No | The paper describes the components of the loss function used in training the Zero-Mean Diffusion model, including `LCVDM` and `Lmean`, and mentions a weighting parameter `ω`. However, it does not provide concrete values for hyperparameters such as learning rate, batch size, number of epochs, or specific optimizer settings. |