PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics

• URL: http://arxiv.org/abs/2103.02015v1
• Date: Tue, 2 Mar 2021 20:37:57 GMT
• Title: PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics
• Authors: Nati Daniel, Ariel Larey, Eliel Aknin, Garrett A. Osswald, Julie M. Caldwell, Mark Rochman, Margaret H. Collins, Guang-Yu Yang, Nicoleta C. Arva, Kelley E. Capocelli, Marc E. Rothenberg, Yonatan Savir
• Abstract summary: 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%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background. Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Disease diagnosis and monitoring requires determining the concentration of eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat subjective task currently performed by pathologists. Methods. Herein, we aimed to use machine learning to identify, quantitate and diagnose EoE. We labeled more than 100M pixels of 4345 images obtained by scanning whole slides of H&E-stained sections of esophageal biopsies derived from 23 EoE patients. We used this dataset to train a multi-label segmentation deep network. To validate the network, we examined a replication cohort of 1089 whole slide images from 419 patients derived from multiple institutions. Findings. PECNet segmented both intact and not-intact eosinophils with a mean intersection over union (mIoU) of 0.93. This segmentation 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%. Using whole slide images from the validation cohort, PECNet achieved an accuracy of 94.8%, sensitivity of 94.3%, and specificity of 95.14% in reporting EoE disease activity. Interpretation. We have developed a deep learning multi-label semantic segmentation network that successfully addresses two of the main challenges in EoE diagnostics and digital pathology, the need to detect several types of small features simultaneously and the ability to analyze whole slides efficiently. Our results pave the way for an automated diagnosis of EoE and can be utilized for other conditions with similar challenges.

Related papers

• Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis [9.044271577557721]
  Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by eosinophil-dominated inflammation. Recent advances have seen AI-assisted endoscopic imaging, guided by the EREFS system, emerge as a potential alternative to reduce reliance on invasive histologic assessments. This study seeks to improve the performance of deep learning-based EoE phenotype classification by augmenting our training data with a diverse set of images from online platforms, public datasets, and electronic textbooks.
  arXiv  Detail & Related papers  (2025-02-06T16:38:47Z)
• Uncertainty Quantification for Eosinophil Segmentation [16.70916787417709]
  Eosinophilic Esophagitis (EoE) is an allergic condition increasing in prevalence. To diagnose EoE, pathologists must find 15 or more eosinophils within a single high-power field (400X magnification). We propose an improvement of Adorno et al's approach for quantifying eosinphils using deep image segmentation.
  arXiv  Detail & Related papers  (2023-09-28T15:49:01Z)
• Artificial Intelligence-based Eosinophil Counting in Gastrointestinal Biopsies [1.7869681025240884]
  Normally eosinophils are present in the gastrointestinal (GI) tract of healthy individuals. When the eosinophils increase beyond their usual amount in the GI tract, a patient gets varied symptoms. Histopathology is the gold standard in the diagnosis for this condition. In this study, we trained and tested a deep neural network based on UNet to detect and count eosinophils in GI tract biopsies.
  arXiv  Detail & Related papers  (2022-11-25T07:18:28Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• 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)
• WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma [51.50991881342181]
  This challenge includes 10,091 patch-level annotations and over 130 million labeled pixels. First place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919)
  arXiv  Detail & Related papers  (2022-04-13T15:27:05Z)
• Label-Assemble: Leveraging Multiple Datasets with Partial Labels [68.46767639240564]
  "Label-Assemble" aims to unleash the full potential of partial labels from an assembly of public datasets. We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
  arXiv  Detail & Related papers  (2021-09-25T02:48:17Z)
• Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features [0.0]
  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%.
  arXiv  Detail & Related papers  (2021-01-13T10:38:46Z)
• 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)
• Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble [64.29529357862955]
  We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
  arXiv  Detail & Related papers  (2020-10-21T18:11:36Z)
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation [95.57532063232198]
  coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries. To control the infection, identifying and separating the infected people is the most crucial step. This paper develops a novel Joint Classification and (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis.
  arXiv  Detail & Related papers  (2020-04-15T12:30:40Z)

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.