Differentiable Imaging Meets Adaptive Neural Dropout: An Advancing Method for Transparent Object Tomography

Label-free tomographic microscopy offers a compelling means to visualize three-dimensional (3D) refractive index (RI) distributions from two-dimensional (2D) intensity measurements. However, limited forward-model accuracy and the ill-posed nature of the inverse problem hamper artifact-free reconstructions. Meanwhile, artificial neural networks excel at modeling nonlinearities. Here, we employ a Differentiable Imaging framework that represents the 3D sample as a multi-layer neural network embedding physical constraints of light propagation. Building on this formulation, we propose a physics-guided Adaptive Dropout Neural Network (ADNN) for optical diffraction tomography (ODT), focusing on network topology and voxel-wise RI fidelity rather than solely on input-output mappings. By exploiting prior knowledge of the sample's RI, the ADNN adaptively drops and reactivates neurons, enhancing reconstruction accuracy and stability. We validate this method with extensive simulations and experiments on weakly and multiple-scattering samples under different imaging setups. The ADNN significantly improves quantitative 3D RI reconstructions, providing superior optical-sectioning and effectively suppressing artifacts. Experimental results show that the ADNN reduces the Mean Absolute Error (MAE) by a factor of 3 to 5 and increases the Structural Similarity Index Metric (SSIM) by about 4 to 30 times compared to the state-of-the-art approach.