arXiv subjects

Vinith Kishore

Publications and source records attributed to Vinith Kishore.

8 recordsLinked to original sources

arXiv2026

Fast Volume Alignment by Frequency-Marched Newton Method

We introduce Matcha, a fast method for rotational pose estimation in three-dimensional alignment, and combine it with FFT-based translation updates for full pose estimation. Classical matched filtering evaluates cross-correlation over a large discretized transformation space; we instead treat rotational alignment as a continuous optimization problem on SO(3). Matcha starts from a bandlimited Wigner-D expansion of the rotational correlation, which enables rapid objective evaluation together with analytic gradients and Hessians. A low-bandwidth SOFFT search provides robust candidate rotations, which are then refined by frequency marching: the angular bandwidth is progressively increased, and candidates are updated by Newton steps at each level. This confines exhaustive search to a single low-frequency stage while allowing the final accuracy to be determined by continuous refinement rather than by the grid spacing. We prove a deterministic conditional guarantee showing that, under reasonable assumptions, Matcha returns a near-optimal solution for the final bandlimited objective. On synthetic rotation-estimation benchmarks, Matcha attains sub-degree accuracy while substantially reducing runtime relative to exhaustive SO(3) search. Integrated into a RELION-5 subtomogram-averaging workflow, it matches the baseline reconstruction quality on the tested dataset, reaching the same Nyquist-limited local resolution while reducing rotational pose-refinement time by more than an order of magnitude.

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arXiv2025

Beyond Exhaustive Sampling: Efficient Rotational Matching via Ball Harmonics

Cryo-ET allows to generate tomograms of biological samples in situ, capturing complex structures in their native context. Despite low signal-to-noise ratio in reconstructed volumes, the large number of copies of the same macromolecules makes it possible to retrieve high-resolution maps by averaging many aligned subtomograms. To keep up with technical advances in the imaging process and the resulting huge amounts of data available, there is a need for scalable, fast and robust procedures to align subtomograms. We propose a subtomogram alignment framework based on the ball harmonics expansion that combines frequency- and gradient-based optimization strategies to avoid exhaustive rotation sampling, enabling a speed-up of an order of magnitude compared to current approaches.

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arXiv2025

Localized Supervised Learning for Cryo-ET Reconstruction

Cryo-electron tomography (Cryo-ET) is a powerful tool in structural biology for 3D visualization of cells and biological systems at resolutions sufficient to identify individual proteins in situ. The measurements are collected by tilting the frozen specimen and exposing it to an electron beam of known dosage. As the biological samples are prone to electron damage, the samples can be exposed to only a limited dosage of electrons, leading to noisy and incomplete measurements. Thus, the reconstructions are noisy and incomplete, leading to the missing wedge problem. Currently, self-supervised learning is used to compensate for this issue. This typically involves, for each volume to recover, training a large 3D UNet on the initial noisy reconstruction, leading to large training time and memory requirements. In this work, we exploit the local nature of the forward model to train a lightweight network using only localized data from the measurements. This design provides flexibility in balancing computational and time requirements while reconstructing the volumes with high accuracy. We observe experimentally that this network can work well on unseen datasets, despite using a network trained on a few measurements.

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arXiv2025

CryoLithe: Rapid Cryo-ET Reconstruction via Transform-Localized Deep Learning

Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular structures. Accurate reconstruction of high-resolution volumes is complicated by the very low signal-to-noise ratio and a restricted range of sample tilts. Recent self-supervised deep learning approaches, which post-process initial reconstructions by filtered backprojection (FBP), have significantly improved reconstruction quality with respect to signal processing iterative algorithms, but they are slow, taking dozens of hours for an expert to reconstruct a tomogram and demand large memory. We present CryoLithe, an end-to-end network that directly estimates the volume from an aligned tilt series. CryoLithe achieves denoising and missing wedge correction comparable or better than state-of-the-art self-supervised deep learning approaches such as Icecream, Cryo-CARE, IsoNet or DeepDeWedge, while being two orders of magnitude faster. To achieve this, we implement a local, memory-efficient reconstruction network. We demonstrate that leveraging transform-domain locality makes our network robust to distribution shifts, enabling effective supervised training and giving excellent results on real data$\unicode{x2013}$without retraining or fine-tuning. CryoLithe reconstructions facilitate downstream cryo-ET analysis, including segmentation and subtomogram averaging and is openly available: https://github.com/swing-research/CryoLithe.

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arXiv2024

Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation

We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections. Furthermore, the implicit network acts as an effective regularizer, allowing for high-quality reconstruction at low signal-to-noise ratios as well as partially restoring the missing wedge information. We compare the performance of ICE-TIDE to existing approaches on realistic simulated volumes where the significant gains in resolution and accuracy of recovering deformations can be precisely evaluated. Finally, we demonstrate ICE-TIDE's ability to perform on experimental data sets.

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arXiv2023

Implicit Reconstructions from Deformed Projections for CryoET

Cryo-electron tomography (cryoET) is a technique that captures images of biological samples at different tilts, preserving their native state as much as possible. Along with the partial tilt series and noise, one of the major challenges in estimating the accurate 3D structure of the sample is the deformations in the images incurred during the acquisition. We model these deformations as continuous operators and estimate the unknown 3D volume using implicit neural representations. This framework allows to easily incorporate the deformation and estimate jointly the deformation parameters and the volume using a standard optimization algorithm. This approach doesn't require training data and can benefit from standard prior in the optimization procedure.

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arXiv2022

Manifold Rewiring for Unlabeled Imaging

Geometric data analysis relies on graphs that are either given as input or inferred from data. These graphs are often treated as "correct" when solving downstream tasks such as graph signal denoising. But real-world graphs are known to contain missing and spurious links. Similarly, graphs inferred from noisy data will be perturbed. We thus define and study the problem of graph denoising, as opposed to graph signal denoising, and propose an approach based on link-prediction graph neural networks. We focus in particular on neighborhood graphs over point clouds sampled from low-dimensional manifolds, such as those arising in imaging inverse problems and exploratory data analysis. We illustrate our graph denoising framework on regular synthetic graphs and then apply it to single-particle cryo-EM where the measurements are corrupted by very high levels of noise. Due to this degradation, the initial graph is contaminated by noise, leading to missing or spurious edges. We show that our proposed graph denoising algorithm improves the state-of-the-art performance of multi-frequency vector diffusion maps.

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arXiv2016

Identifying Diabetic Patients with High Risk of Readmission

Hospital readmissions are expensive and reflect the inadequacies in healthcare system. In the United States alone, treatment of readmitted diabetic patients exceeds 250 million dollars per year. Early identification of patients facing a high risk of readmission can enable healthcare providers to to conduct additional investigations and possibly prevent future readmissions. This not only improves the quality of care but also reduces the medical expenses on readmission. Machine learning methods have been leveraged on public health data to build a system for identifying diabetic patients facing a high risk of future readmission. Number of inpatient visits, discharge disposition and admission type were identified as strong predictors of readmission. Further, it was found that the number of laboratory tests and discharge disposition together predict whether the patient will be readmitted shortly after being discharged from the hospital (i.e. <30 days) or after a longer period of time (i.e. >30 days). These insights can help healthcare providers to improve inpatient diabetic care. Finally, the cost analysis suggests that \$252.76 million can be saved across 98,053 diabetic patient encounters by incorporating the proposed cost sensitive analysis model.

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