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Found 13 papers, 8 papers with code
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications, but achieving clinically viable accuracy and computational efficiency is still an unsolved challenge.
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image
Unlike in visual analysis, pixel values in quantitative images correspond to physical metrics measured by specific devices (e. g., a depth image).
Learning truly monotone operators with applications to nonlinear inverse problems
This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss.
A foundation for exact binarized morphological neural networks
Training and running deep neural networks (NNs) often demands a lot of computation and energy-intensive specialized hardware (e. g. GPU, TPU...).
On the detection of Out-Of-Distribution samples in Multiple Instance Learning
While significant efforts have been devoted to OOD detection in classical supervised settings, the context of weakly supervised learning, particularly the Multiple Instance Learning (MIL) framework, remains under-explored.
Multiple Instance Learning
Out of Distribution (OOD) Detection
+1
Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography
The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0. 880 and 0. 920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3. 27 to 3. 79% for four measurements with different poses.
MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume
We propose a method (named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray image, a low-cost, low-radiation, and highly accessible imaging modality, through musculoskeletal decomposition leveraging fine-grained segmentation in CT. We train a multi-channel quantitative image translation model to decompose an X-ray image into projections of CT of individual muscles to infer the lean muscle mass and muscle volume.
Binary Multi Channel Morphological Neural Network
Neural networks and particularly Deep learning have been comparatively little studied from the theoretical point of view.
Binary Morphological Neural Network
In the last ten years, Convolutional Neural Networks (CNNs) have formed the basis of deep-learning architectures for most computer vision tasks.
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification
Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details.
Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation
Image registration and segmentation are the two most studied problems in medical image analysis.
Atlas-based automated detection of swim bladder in Medaka embryo
An automated random forest clas-sifier is built from these descriptors in order to classify embryos with and without a swim bladder.
Spatially variant PSF modeling in confocal macroscopy
Point spread function (PSF) plays an essential role in image reconstruction.
