Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e. g. via Dual-energy X-ray Absorptiometry (DXA).
In this work, we propose a computer-aided diagnosis approach to provide more accurate and consistent assessments of both composite and fine-grained OA grades simultaneously.
no code implementations • 29 Apr 2021 • Xiao-Yun Zhou, Bolin Lai, Weijian Li, Yirui Wang, Kang Zheng, Fakai Wang, ChiHung Lin, Le Lu, Lingyun Huang, Mei Han, Guotong Xie, Jing Xiao, Kuo Chang-Fu, Adam Harrison, Shun Miao
It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student SSL mechanism.
In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.
Specifically, we propose a new semi-supervised self-training algorithm to train the BMD regression model using images coupled with DEXA measured BMDs and unlabeled images with pseudo BMDs.
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotations.
This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification.
In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.
Accurate segmentation of critical anatomical structures is at the core of medical image analysis.
A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences).
Image landmark detection aims to automatically identify the locations of predefined fiducial points.
The goal of this paper is to address the challenging problem of automatically identifying the underlying full design of curve patterns from such a sherd.
In particular, we design a Mask Weight Network (MWN) to learn a set of masks and then apply channel-wise masking operations to ROI feature map, followed by a global pooling and a cheap fully-connected layer.
In this paper, we attempt to utilize 3D human-skeleton sequences to learn a model that can extract view-invariant motion features from optical flows in different views.
In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object.
We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).