Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging

• URL: http://arxiv.org/abs/2407.21381v1
• Date: Wed, 31 Jul 2024 07:12:06 GMT
• Title: Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging
• Authors: Wenhua Wu, Kun Hu, Wenxi Yue, Wei Li, Milena Simic, Changyang Li, Wei Xiang, Zhiyong Wang,
• Abstract summary: Knee osteoarthritis (KOA) is a common form of arthritis that causes physical disability. Computer-aided techniques to automatically assess KOA severity and progression can greatly benefit KOA treatment and disease management. In this study, a novel generative model is proposed, namely Identity-Consistent Radiographic Diffusion Network (IC-RDN), for multifaceted KOA prognosis.
• Score: 22.005283322766832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knee osteoarthritis (KOA), a common form of arthritis that causes physical disability, has become increasingly prevalent in society. Employing computer-aided techniques to automatically assess the severity and progression of KOA can greatly benefit KOA treatment and disease management. Particularly, the advancement of X-ray technology in KOA demonstrates its potential for this purpose. Yet, existing X-ray prognosis research generally yields a singular progression severity grade, overlooking the potential visual changes for understanding and explaining the progression outcome. Therefore, in this study, a novel generative model is proposed, namely Identity-Consistent Radiographic Diffusion Network (IC-RDN), for multifaceted KOA prognosis encompassing a predicted future knee X-ray scan conditioned on the baseline scan. Specifically, an identity prior module for the diffusion and a downstream generation-guided progression prediction module are introduced. Compared to conventional image-to-image generative models, identity priors regularize and guide the diffusion to focus more on the clinical nuances of the prognosis based on a contrastive learning strategy. The progression prediction module utilizes both forecasted and baseline knee scans, and a more comprehensive formulation of KOA severity progression grading is expected. Extensive experiments on a widely used public dataset, OAI, demonstrate the effectiveness of the proposed method.

Related papers

• Multi-task Learning Approach for Intracranial Hemorrhage Prognosis [0.0]
  We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input.
  arXiv  Detail & Related papers  (2024-08-16T14:56:17Z)
• Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks [0.11249583407496219]
  We trained a CycleGAN model to synthesize past and future stages of Knee Osteoarthritis (KOA) on any genuine radiograph. The model was particularly effective in future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages.
  arXiv  Detail & Related papers  (2023-11-10T00:15:00Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers [2.9822184411723645]
  Knee Osteoarthritis (KOA) is a highly prevalent chronic musculoskeletal condition with no currently available treatment. We leveraged recent advances in Deep Learning and developed a unified framework for the multi-modal fusion of knee imaging data. Our follow-up analysis generally shows that prediction from the imaging data is more accurate for post-traumatic subjects.
  arXiv  Detail & Related papers  (2023-07-03T09:10:57Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders [65.95959936242993]
  We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
  arXiv  Detail & Related papers  (2022-03-20T21:00:10Z)
• Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network [8.950918531231158]
  This paper presents a deep learning-based framework, namely OsteoHRNet, that automatically assesses the Knee Osteoarthritis severity in terms of Kellgren and Lawrence grade classification from X-rays. Our proposed model has achieved the best multiclass accuracy of 71.74% and MAE of 0.311 on the baseline cohort of the OAI dataset.
  arXiv  Detail & Related papers  (2021-06-27T17:29:46Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.