Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection

• URL: http://arxiv.org/abs/2110.13254v1
• Date: Mon, 25 Oct 2021 20:39:28 GMT
• Title: Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection
• Authors: Weiyao Wang, Aniruddha Tamhane, Christine Santos, John R. Rzasa, James H. Clark, Therese L. Canares, and Mathias Unberath
• Abstract summary: We present a two stage method that first, identifies valid frames by detecting and extracting ear drum patches from the video sequence. Second, performs the proposed shift contrastive anomaly detection to flag the otoscopy video sequences as normal or abnormal. Our method achieves an AUROC of 88.0% on the patient-level and also outperforms the average of a group of 25 clinicians in a comparative study.
• Score: 4.922640055654283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ear related concerns and symptoms represents the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered disease of the middle and external presents significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitating the presence or absence of ear pathology to be determined clinically. Research has however demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. With recent advances in computer vision and machine learning, there is an increasing interest in helping clinicians to accurately diagnose middle and external ear pathology with computer-aided systems. It has been shown that AI has the capacity to analyse a single clinical image captured during examination of the ear canal and eardrum from which it can determine the likelihood of a pathognomonic pattern for a specific diagnosis being present. The capture of such an image can however be challenging especially to inexperienced clinicians. To help mitigate this technical challenge we have developed and tested a method using video sequences. We present a two stage method that first, identifies valid frames by detecting and extracting ear drum patches from the video sequence, and second, performs the proposed shift contrastive anomaly detection to flag the otoscopy video sequences as normal or abnormal. Our method achieves an AUROC of 88.0% on the patient-level and also outperforms the average of a group of 25 clinicians in a comparative study, which is the largest of such published to date. We conclude that the presented method achieves a promising first step towards automated analysis of otoscopy video.

Related papers

• Selfsupervised learning for pathological speech detection [0.0]
  Speech production is susceptible to influence and disruption by various neurodegenerative pathological speech disorders. These disorders lead to pathological speech characterized by abnormal speech patterns and imprecise articulation. Unlike neurotypical speakers, patients with speech pathologies or impairments are unable to access various virtual assistants such as Alexa, Siri, etc.
  arXiv  Detail & Related papers  (2024-05-16T07:12:47Z)
• A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions [6.728206045751265]
  This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness. It covers both clinical and automated approaches for detecting retinal disease, focusing on studies from the past decade.
  arXiv  Detail & Related papers  (2023-11-22T22:10:53Z)
• Show from Tell: Audio-Visual Modelling in Clinical Settings [58.88175583465277]
  We consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations without human expert annotation. A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose. The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference.
  arXiv  Detail & Related papers  (2023-10-25T08:55:48Z)
• Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination [12.75726717324889]
  Sonographers usually discriminate different nodules by examining the nodule features and the surrounding structures. We built a reidentification system that consists of two parts: an extractor based on the deep learning model that can extract feature vectors from the input video clips and a real-time clustering algorithm that automatically groups feature vectors by nodules.
  arXiv  Detail & Related papers  (2023-10-10T06:20:14Z)
• Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning [5.201499435914216]
  Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. Previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We contribute a new end-to-end MIL approach with two key methodological innovations.
  arXiv  Detail & Related papers  (2023-05-25T18:22:12Z)
• A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography [0.0]
  OCT and OCTA are promising tools for the (early) diagnosis of Alzheimer's disease (AD) interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. The current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA.
  arXiv  Detail & Related papers  (2022-09-07T08:27:10Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

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.