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Found 18 papers, 6 papers with code
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data
In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution.
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans
In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D.
Effective Quantization for Diffusion Models on CPUs
Diffusion models have gained popularity for generating images from textual descriptions.
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis
Alice introduces a new contrastive learning strategy which encourages the similarity between views that are diversely mined but with consistent high-level semantics, in order to learn invariant anatomical features.
ReLeaPS : Reinforcement Learning-based Illumination Planning for Generalized Photometric Stereo
Illumination planning in photometric stereo aims to find a balance between tween surface normal estimation accuracy and image capturing efficiency by selecting optimal light configurations.
DiLiGenT-Pi: Photometric Stereo for Planar Surfaces with Rich Details - Benchmark Dataset and Beyond
Photometric stereo aims to recover detailed surface shapes from images captured under varying illuminations.
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs.
NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition
In this paper, we propose a deep neural network named NeuralMPS to solve the MPS problem under general non-Lambertian spectral reflectances.
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving
How to model action sequences so that one can further consider the effects of different action parameters in the scenario is the bottleneck of the problem.
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation
Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs.
Edge-preserving Near-light Photometric Stereo with Neural Surfaces
This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction.
Multispectral Photometric Stereo for Spatially-Varying Spectral Reflectances: A Well Posed Problem?
To make the problem well-posed, existing MPS methods rely on restrictive assumptions, such as shape prior, surfaces having a monochromatic with uniform albedo.
Local-to-Global Contraction in Simplicial Complexes
We give a local-to-global principle for relative entropy contraction in simplicial complexes.
Data Structures and Algorithms Probability
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently.
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)
We observed that CSTA achieves the maximum mean accuracy of 97. 43$\pm$2. 26 % and 85. 71$\pm$13. 41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis.
X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too.
Modified log-Sobolev inequalities for strongly log-concave distributions
We show that the modified log-Sobolev constant for a natural Markov chain which converges to an $r$-homogeneous strongly log-concave distribution is at least $1/r$.
Probability Data Structures and Algorithms Combinatorics
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
For Markov chain Monte Carlo methods, one of the greatest discrepancies between theory and system is the scan order - while most theoretical development on the mixing time analysis deals with random updates, real-world systems are implemented with systematic scans.
