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.
DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.
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.
Object detection methods are widely adopted for computer-aided diagnosis using medical images.
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
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.
To this end, we present a deep HMLC approach for CXR CAD.
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.
To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images.
In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining.
In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans.
Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues.
The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).