Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology

• URL: http://arxiv.org/abs/2303.07771v3
• Date: Tue, 18 Apr 2023 10:33:29 GMT
• Title: Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
• Authors: Rao Muhammad Umer, Armin Gruber, Sayedali Shetab Boushehri, Christian Metak, Carsten Marr
• Abstract summary: We train a robust CNN for WBC classification by addressing cross-domain data imbalance and domain shifts. Our approach achieves the best F1 macro score compared to other existing methods.
• Score: 3.7007225479462402
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate morphological classification of white blood cells (WBCs) is an important step in the diagnosis of leukemia, a disease in which nonfunctional blast cells accumulate in the bone marrow. Recently, deep convolutional neural networks (CNNs) have been successfully used to classify leukocytes by training them on single-cell images from a specific domain. Most CNN models assume that the distributions of the training and test data are similar, i.e., the data are independently and identically distributed. Therefore, they are not robust to different staining procedures, magnifications, resolutions, scanners, or imaging protocols, as well as variations in clinical centers or patient cohorts. In addition, domain-specific data imbalances affect the generalization performance of classifiers. Here, we train a robust CNN for WBC classification by addressing cross-domain data imbalance and domain shifts. To this end, we use two loss functions and demonstrate their effectiveness in out-of-distribution (OOD) generalization. Our approach achieves the best F1 macro score compared to other existing methods and is able to consider rare cell types. This is the first demonstration of imbalanced domain generalization in hematological cytomorphology and paves the way for robust single cell classification methods for the application in laboratories and clinics.

Related papers

• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images [40.347953893940044]
  We introduce a novel approach for white blood cell classification based on neural cellular automata (NCA) Our NCA-based method is significantly smaller in terms of parameters and exhibits robustness to domain shifts. Our results demonstrate that NCA can be used for image classification, and they address key challenges of conventional methods.
  arXiv  Detail & Related papers  (2024-04-08T14:59:53Z)
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Domain Invariant Model with Graph Convolutional Network for Mammogram Classification [49.691629817104925]
  We propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN) We first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN)
  arXiv  Detail & Related papers  (2022-04-21T08:23:44Z)
• Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features [0.0]
  White Blood Cell (WBC) Leukaemia is detected through image-based classification. Convolutional Neural Networks are used to learn the features needed to classify images of cells a malignant or healthy. This type of model requires learning a large number of parameters and is difficult to interpret and explain. We present an XAI model which uses only 24 explainable and interpretable features and is highly competitive to other approaches by outperforming them by about 4.38%.
  arXiv  Detail & Related papers  (2022-01-28T00:08:56Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks [4.095759108304108]
  This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs) Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, has outputted a weighted F1-score of 88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary test set.
  arXiv  Detail & Related papers  (2021-05-09T18:58:48Z)
• Attention based Multiple Instance Learning for Classification of Blood Cell Disorders [38.086308180994976]
  We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. With the features extracted for each cell, a multiple instance learning method classifies patient samples into one out of four blood cell disorders. The attention mechanism provides a measure of the contribution of each cell to the overall classification and significantly improves the network's classification accuracy as well as its interpretability for the medical expert.
  arXiv  Detail & Related papers  (2020-07-22T19:29:40Z)
• Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification [8.121462458089143]
  A misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. In the past decade, many approaches have been proposed for classifying human red blood cell morphology. A class imbalance problem can lead to a biased model towards the majority class.
  arXiv  Detail & Related papers  (2020-01-10T07:31:57Z)

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.