Outlier Detection in Large Radiological Datasets using UMAP

• URL: http://arxiv.org/abs/2407.21263v2
• Date: Thu, 1 Aug 2024 01:59:39 GMT
• Title: Outlier Detection in Large Radiological Datasets using UMAP
• Authors: Mohammad Tariqul Islam, Jason W. Fleischer,
• Abstract summary: In biomedical data, variations in image quality, labeling, reports, and archiving can lead to errors, inconsistencies, and repeated samples. Here, we show that the uniform manifold approximation and projection algorithm can find these anomalies essentially by forming independent clusters. While the results are archival and retrospective, the graph-based methods work for any data type and will prove equally beneficial for curation at the time of dataset creation.
• Score: 1.206248959194646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of machine learning algorithms heavily relies on the quality of samples and the accuracy of their corresponding labels. However, building and maintaining large, high-quality datasets is an enormous task. This is especially true for biomedical data and for meta-sets that are compiled from smaller ones, as variations in image quality, labeling, reports, and archiving can lead to errors, inconsistencies, and repeated samples. Here, we show that the uniform manifold approximation and projection (UMAP) algorithm can find these anomalies essentially by forming independent clusters that are distinct from the main (good) data but similar to other points with the same error type. As a representative example, we apply UMAP to discover outliers in the publicly available ChestX-ray14, CheXpert, and MURA datasets. While the results are archival and retrospective and focus on radiological images, the graph-based methods work for any data type and will prove equally beneficial for curation at the time of dataset creation.

Related papers

• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• How Can We Tame the Long-Tail of Chest X-ray Datasets? [0.0]
  Chest X-rays (CXRs) are a medical imaging modality that is used to infer a large number of abnormalities. Few of them are quite commonly observed and are abundantly represented in CXR datasets. It is challenging for current models to learn independent discriminatory features for labels that are rare but may be of high significance.
  arXiv  Detail & Related papers  (2023-09-08T12:28:40Z)
• DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets [4.833815605196965]
  This paper presents a novel method for addressing data imbalance in machine learning. It computes sample likelihoods based on image appearance using deep perceptual embeddings and clustering. It then uses these likelihoods to weigh samples differently during training with a proposed $bfGeneralized Focal Loss$ function.
  arXiv  Detail & Related papers  (2023-08-19T02:11:49Z)
• Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects [82.20189909620899]
  Estimating heterogeneous treatment effects is an important problem across many domains. Currently, most existing works rely exclusively on observational data. We propose to estimate heterogeneous treatment effects by combining large amounts of observational data and small amounts of randomized data.
  arXiv  Detail & Related papers  (2022-02-25T18:59:54Z)
• Assessing the Quality of the Datasets by Identifying Mislabeled Samples [14.881597737762316]
  We propose a novel statistic -- noise score -- as a measure for the quality of each data point to identify mislabeled samples. In our work, we use the representations derived by the inference network of data quality supervised variational autoencoder (AQUAVS) We validate our proposed statistic through experimentation by corrupting MNIST, FashionMNIST, and CIFAR10/100 datasets.
  arXiv  Detail & Related papers  (2021-09-10T17:14:09Z)
• Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities [0.6882042556551609]
  Semi-supervised deep learning is an attractive alternative to large labelled datasets. In real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset. This results in a distribution mismatch between the unlabelled and labelled datasets.
  arXiv  Detail & Related papers  (2021-08-17T00:35:43Z)
• Self-Trained One-class Classification for Unsupervised Anomaly Detection [56.35424872736276]
  Anomaly detection (AD) has various applications across domains, from manufacturing to healthcare. In this work, we focus on unsupervised AD problems whose entire training data are unlabeled and may contain both normal and anomalous samples. To tackle this problem, we build a robust one-class classification framework via data refinement. We show that our method outperforms state-of-the-art one-class classification method by 6.3 AUC and 12.5 average precision.
  arXiv  Detail & Related papers  (2021-06-11T01:36:08Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures [83.48996461770017]
  This work presents a Deep Learning method based on the late fusion of different convolutional architectures. We built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool.
  arXiv  Detail & Related papers  (2020-12-23T14:38:35Z)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)

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.