Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset

• URL: http://arxiv.org/abs/2408.13542v1
• Date: Sat, 24 Aug 2024 10:14:52 GMT
• Title: Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset
• Authors: Ammar Ahmed, Ali Shariq Imran, Zenun Kastrati, Sher Muhammad Daudpota, Mohib Ullah, Waheed Noord,
• Abstract summary: Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. We propose a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention.
• Score: 4.391219238034502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wrist pathologies, {particularly fractures common among children and adolescents}, present a critical diagnostic challenge. While X-ray imaging remains a prevalent diagnostic tool, the increasing misinterpretation rates highlight the need for more accurate analysis, especially considering the lack of specialized training among many surgeons and physicians. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. However, distinguishing subtle variations between {pediatric} wrist pathologies in X-rays remains challenging. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. {In this paper, we address the challenge of pediatric wrist pathology recognition with a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention. We refine our fine-grained architecture through ablation analysis and the integration of LION.} Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. {Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition. The implementation code can be found at https://github.com/ammarlodhi255/fine-grained-approach-to-wrist-pathology-recognition

Related papers

• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• Anatomy-Driven Pathology Detection on Chest X-rays [17.670821896026137]
  We propose anatomy-driven pathology detection (ADPD), which uses easy-to-annotate bounding boxes of anatomical regions as proxies for pathologies. We study two training approaches: supervised training using anatomy-level pathology labels and multiple instance learning (MIL) with image-level pathology labels. Our results show that our anatomy-level training approach outperforms weakly supervised methods and fully supervised detection with limited training samples.
  arXiv  Detail & Related papers  (2023-09-05T20:58:15Z)
• 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)
• Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs [0.0]
  We propose a multi-label disease prediction model that allows the detection of more than one pathology at a given test time. Our proposed model achieved the highest AUC score of 0.896 for the condition Cardiomegaly.
  arXiv  Detail & Related papers  (2022-02-08T00:43:57Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• 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)
• Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks [0.0]
  We propose a two-step deep-learning framework to partially automate the detection of stenosis from X-ray coronary angiography images. We achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary Artery angle view and 0.68/0.73 recall on the determination of the regions of interest, for LCA and RCA, respectively.
  arXiv  Detail & Related papers  (2021-03-04T11:45:54Z)
• 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)
• AI Progress in Skin Lesion Analysis [0.0]
  Problems of AI bias regarding the lack of skin images in dark individuals, being able to accurately detect, delineate, and segment lesions or regions of interest, and low shot learning. We report skin analysis algorithms that gracefully degrade and still perform well at low shots, when compared to baseline algorithms.
  arXiv  Detail & Related papers  (2020-09-28T13:44:50Z)

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.