Fluorescence microscopy is a major driver of scientific progress in the life sciences. Although high-end confocal microscopes are capable of filtering out-of-focus light, cheaper and more accessible microscopy modalities, such as widefield microscopy, can not, which consequently leads to hazy image data. Computational dehazing is trying to combine the best of both worlds, leading to cheap microscopy but crisp-looking images. The perception-distortion trade-off tells us that we can optimize either for data fidelity, e.g. low MSE or high PSNR, or for data realism, measured by perceptual metrics such as LPIPS or FID. Existing methods either prioritize fidelity at the expense of realism, or produce perceptually convincing results that lack quantitative accuracy. In this work, we propose HazeMatching, a novel iterative method for dehazing light microscopy images, which effectively balances these objectives. Our goal was to find a balanced trade-off between the fidelity of the dehazing results and the realism of individual predictions (samples). We achieve this by adapting the conditional flow matching framework by guiding the generative process with a hazy observation in the conditional velocity field. We evaluate HazeMatching on 5 datasets, covering both synthetic and real data, assessing both distortion and perceptual quality. Our method is compared against 11 baselines, achieving a consistent balance between fidelity and realism on average. Additionally, with calibration analysis, we show that HazeMatching produces well-calibrated predictions. Note that our method does not need an explicit degradation operator to exist, making it easily applicable on real microscopy data. All data used for training and evaluation and our code will be publicly available under a permissive license.

Precise pose estimation of optical microrobots is essential for enabling high-precision object tracking and autonomous biological studies. However, current methods rely heavily on large, high-quality microscope image datasets, which are difficult and costly to acquire due to the complexity of microrobot fabrication and the labour-intensive labelling. Digital twin systems offer a promising path for sim-to-real data augmentation, yet existing techniques struggle to replicate complex optical microscopy phenomena, such as diffraction artifacts and depth-dependent imaging.This work proposes a novel physics-informed deep generative learning framework that, for the first time, integrates wave optics-based physical rendering and depth alignment into a generative adversarial network (GAN), to synthesise high-fidelity microscope images for microrobot pose estimation efficiently. Our method improves the structural similarity index (SSIM) by 35.6% compared to purely AI-driven methods, while maintaining real-time rendering speeds (0.022 s/frame).The pose estimator (CNN backbone) trained on our synthetic data achieves 93.9%/91.9% (pitch/roll) accuracy, just 5.0%/5.4% (pitch/roll) below that of an estimator trained exclusively on real data. Furthermore, our framework generalises to unseen poses, enabling data augmentation and robust pose estimation for novel microrobot configurations without additional training data.

We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (Fourier-Nets).

While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost solutions also face critical limitations: automated microscopes struggle with consistent focus across varying tissue morphology, traditional auto-focus methods require time-consuming focal stacks, and existing deep-learning approaches either need multiple input images or lack generalization capability across tissue types and staining protocols. We introduce a novel automated microscopic system powered by DeepAf, a novel auto-focus framework that uniquely combines spatial and spectral features through a hybrid architecture for single-shot focus prediction. The proposed network automatically regresses the distance to the optimal focal point using the extracted spatiospectral features and adjusts the control parameters for optimal image outcomes. Our system transforms conventional microscopes into efficient slide scanners, reducing focusing time by 80% compared to stack-based methods while achieving focus accuracy of 0.18 μm on the same-lab samples, matching the performance of dual-image methods (0.19 μm) with half the input requirements. DeepAf demonstrates robust cross-lab generalization with only 0.72% false focus predictions and 90% of predictions within the depth of field. Through an extensive clinical study of 536 brain tissue samples, our system achieves 0.90 AUC in cancer classification at 4x magnification, a significant achievement at lower magnification than typical 20x WSI scans. This results in a comprehensive hardware-software design enabling accessible, real-time digital pathology in resource-constrained settings while maintaining diagnostic accuracy.

A hatter and a railway clerk's 1925 medical breakthrough became one of the most profound events in medical history. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1925,, one of the world's most prestigious medical journals, published a blockbuster finding so significant that its editors offered a rare prelude: "The two communications which follow mark an event in the history of medicine . They form a detailed description of a prolonged and intensive research into the origin of malignant new growths, and they may present a solution of the central problem of cancer." On the day the studies were scheduled to be released, word began to spread beyond the scientific community.

The field of optical microscopy spans across numerous industries and research domains, ranging from education to healthcare, quality inspection and analysis. Nonetheless, a key limitation often cited by optical microscopists refers to the limit of its lateral resolution (typically defined as ~200nm), with potential circumventions involving either costly external modules (e.g. confocal scan heads, etc) and/or specialized techniques [e.g. super-resolution (SR) fluorescent microscopy]. Addressing these challenges in a normal (non-specialist) context thus remains an aspect outside the scope of most microscope users & facilities. This study thus seeks to evaluate an alternative & economical approach to achieving SR optical microscopy, involving non-fluorescent phase-modulated microscopical modalities such as Zernike phase contrast (PCM) and differential interference contrast (DIC) microscopy. Two in silico deep neural network (DNN) architectures which we developed previously (termed O-Net and Theta-Net) are assessed on their abilities to resolve a custom-fabricated test target containing nanoscale features calibrated via atomic force microscopy (AFM). The results of our study demonstrate that although both O-Net and Theta-Net seemingly performed well when super-resolving these images, they were complementary (rather than competing) approaches to be considered for image SR, particularly under different image signal-to-noise ratios (SNRs). High image SNRs favoured the application of O-Net models, while low SNRs inclined preferentially towards Theta-Net models. These findings demonstrate the importance of model architectures (in conjunction with the source image SNR) on model performance and the SR quality of the generated images where DNN models are utilized for non-fluorescent optical nanoscopy, even where the same training dataset & number of epochs are being used.

Optical microrobots actuated by optical tweezers (OT) offer great potential for biomedical applications such as cell manipulation and microscale assembly. These tasks demand accurate three-dimensional perception to ensure precise control in complex and dynamic biological environments. However, the transparent nature of microrobots and low-contrast microscopic imaging challenge conventional deep learning methods, which also require large annotated datasets that are costly to obtain. To address these challenges, we propose a physics-informed, data-efficient framework for depth estimation of optical microrobots. Our method augments convolutional feature extraction with physics-based focus metrics, such as entropy, Laplacian of Gaussian, and gradient sharpness, calculated using an adaptive grid strategy. This approach allocates finer grids over microrobot regions and coarser grids over background areas, enhancing depth sensitivity while reducing computational complexity. We evaluate our framework on multiple microrobot types and demonstrate significant improvements over baseline models. Specifically, our approach reduces mean squared error (MSE) by over 60% and improves the coefficient of determination (R^2) across all test cases. Notably, even when trained on only 20% of the available data, our model outperforms ResNet50 trained on the full dataset, highlighting its robustness under limited data conditions. Our code is available at: https://github.com/LannWei/CBS2025.

Recent advancements in machine learning have significantly enhanced the precision and efficiency of data-driven methodologies in scientific applications. These methods have found applications in a variety of fields, including physics, medicine, and space sciences, where they help addressing complex challenges which require high-precision measurements. One such application is directional dark matter search experiments that require precise measurements of ions recoiling after their interactions with dark matter particles [1, 2]. Due to their extremely low kinetic energies, in the 1 100 keV range, recoiling ions produce tracks ranging from a few millimeters in gases at low pressure to a few hundreds of nanometers in solids [2, 3]. Taking into account that the required detector mass in practice amounts to several tons, the choice of solid materials as a sensitive medium is advantageous.

Single-pixel imaging has emerged as a key technique in fluorescence microscopy, where fast acquisition and reconstruction are crucial. In this context, images are reconstructed from linearly compressed measurements. In practice, total variation minimisation is still used to reconstruct the image from noisy measurements of the inner product between orthogonal sampling pattern vectors and the original image data. However, data can be leveraged to learn the measurement vectors and the reconstruction process, thereby enhancing compression, reconstruction quality, and speed. We train an autoencoder through self-supervision to learn an encoder (or measurement matrix) and a decoder. We then test it on physically acquired multispectral and intensity data. During acquisition, the learned encoder becomes part of the physical device. Our approach can enhance single-pixel imaging in fluorescence microscopy by reducing reconstruction time by two orders of magnitude, achieving superior image quality, and enabling multispectral reconstructions. Ultimately, learned single-pixel fluorescence microscopy could advance diagnosis and biological research, providing multispectral imaging at a fraction of the cost.