Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features

• URL: http://arxiv.org/abs/2101.04989v1
• Date: Wed, 13 Jan 2021 10:38:46 GMT
• Title: Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features
• Authors: Tomer Czyzewski, Nati Daniel, Mark Rochman, Julie M. Caldwell, Garrett A. Osswald, Margaret H. Collins, Marc E. Rothenberg, and Yonatan Savir
• Abstract summary: Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. One of the main challenges in automating this process is detecting features that are small relative to the size of the biopsy. We developed a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Goal: Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies - a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. Results: In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. Conclusions: We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics.

Related papers

• Cross-organ Deployment of EOS Detection AI without Retraining: Feasibility and Limitation [6.200516824977507]
  Chronic rhinosinusitis (CRS) is characterized by persistent inflammation in the paranasal sinuses. Eos, a crucial component in the mucosal immune response, have been linked to disease severity in CRS. The diagnosis of eosinophilic CRS typically uses a threshold of 10-20 eos per high-power field (HPF)
  arXiv  Detail & Related papers  (2024-11-24T18:01:13Z)
• Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training [3.2646075700744928]
  Histo whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into six broad tissue regions.
  arXiv  Detail & Related papers  (2024-09-29T07:08:45Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting [46.45578907156356]
  We setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. Our findings suggest that nuclei and eosinophils play an important role in the tumour microevironment.
  arXiv  Detail & Related papers  (2023-03-11T01:21:13Z)
• Harnessing Artificial Intelligence to Infer Novel Spatial Biomarkers for the Diagnosis of Eosinophilic Esophagitis [0.0]
  Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. EoE diagnosis requires enumerating the density of esophageal eosinophils in esophageal biopsies. Here, we develop an artificial intelligence platform that infers biomarkers based on semantic segmentation of intact eosinophils and basal zone distributions.
  arXiv  Detail & Related papers  (2022-05-26T18:59:47Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics [0.0]
  Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Herein, we aimed to use machine learning to identify, quantitate and diagnose EoE. PECNet was able to quantitate intact eosinophils with a mean absolute error of 0.611 eosinophils and classify EoE disease activity with an accuracy of 98.5%.
  arXiv  Detail & Related papers  (2021-03-02T20:37:57Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision [0.7915536524413249]
  Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression.
  arXiv  Detail & Related papers  (2021-01-13T20:01:48Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)

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.