Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning

• URL: http://arxiv.org/abs/2207.04565v1
• Date: Sun, 10 Jul 2022 23:30:05 GMT
• Title: Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning
• Authors: Kleanthis Avramidis, Mohammad Rostami, Melinda Chang, Shrikanth Narayanan
• Abstract summary: Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema.
• Score: 36.655566061309734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema. Our approach is based on optic disc localization and detection of explainable papilledema indicators through data augmentation. Experiments on real-world clinical data demonstrate that our proposed method is effective with a diagnostic accuracy comparable to expert ophthalmologists.

Related papers

• Diagnosis of diabetic retinopathy using machine learning & deep learning technique [0.4218593777811082]
  We propose a novel method for fundus detection using object detection and machine learning classification techniques. We use a YOLO_V8 to perform object detection on fundus images and locate the regions of interest (ROIs) such as optic disc, optic cup and lesions. We then use machine learning SVM classification algorithms to classify the ROIs into different DR stages based on the presence or absence of pathological signs.
  arXiv  Detail & Related papers  (2024-11-25T10:09:37Z)
• Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging [59.79875531898648]
  DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
  arXiv  Detail & Related papers  (2023-03-23T18:50:18Z)
• Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases [0.0]
  This study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. We have proposed a clinician-in-the-loop assisted intelligent workflow that performs a retinal vascular assessment on the fundus images. The semiautomatic methodology aims to have a federated approach to AI in healthcare applications with more inputs and interpretations from clinicians.
  arXiv  Detail & Related papers  (2022-12-14T07:00:31Z)
• Deepfake histological images for enhancing digital pathology [0.40631409309544836]
  We develop a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigate the ability of this framework in synthesizing realistic prostate and colon tissue images. We extend the approach to significantly more complex images from colon biopsies and show that the complex microenvironment in such tissues can also be reproduced.
  arXiv  Detail & Related papers  (2022-06-16T17:11:08Z)
• A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection [0.0]
  Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. This paper presents a model for automatic DR classification on eye fundus images.
  arXiv  Detail & Related papers  (2021-10-14T22:04:59Z)
• 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)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• 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)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

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.