WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes

• URL: http://arxiv.org/abs/2306.13531v2
• Date: Tue, 26 Dec 2023 04:58:14 GMT
• Title: WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
• Authors: Satoshi Tsutsui, Winnie Pang, Bihan Wen
• Abstract summary: This paper introduces comprehensive annotations for White Blood Cells (WBCs) images. We have identified 11 morphological attributes associated with the cell and its components. We conduct experiments to predict these attributes from images, providing insights beyond basic WBC classification.
• Score: 22.423647778787334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics, influencing a wide range of medical conditions. For instance, an in-depth study of White Blood Cells (WBCs), a crucial component of our blood, is essential for diagnosing blood-related diseases such as leukemia and anemia. While multiple datasets containing WBC images have been proposed, they mostly focus on cell categorization, often lacking the necessary morphological details to explain such categorizations, despite the importance of explainable artificial intelligence (XAI) in medical domains. This paper seeks to address this limitation by introducing comprehensive annotations for WBC images. Through collaboration with pathologists, a thorough literature review, and manual inspection of microscopic images, we have identified 11 morphological attributes associated with the cell and its components (nucleus, cytoplasm, and granules). We then annotated ten thousand WBC images with these attributes. Moreover, we conduct experiments to predict these attributes from images, providing insights beyond basic WBC classification. As the first public dataset to offer such extensive annotations, we also illustrate specific applications that can benefit from our attribute annotations. Overall, our dataset paves the way for interpreting WBC recognition models, further advancing XAI in the fields of pathology and hematology.

Related papers

• Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model [6.41413650593808]
  We present a novel approach for classifying blood cell images known as BC-SAM. BC-SAM incorporates a fine-tuning technique using LoRA, allowing it to extract general image embeddings from blood cell images. To enhance the applicability of BC-SAM across different blood cell image datasets, we introduce an unsupervised cross-domain autoencoder.
  arXiv  Detail & Related papers  (2024-08-13T08:20:47Z)
• DAFFNet: A Dual Attention Feature Fusion Network for Classification of White Blood Cells [2.0005570775461567]
  We propose a novel dual-branch network Dual Attention Feature Fusion Network (DAFFNet), which integrates the high-level semantic features with morphological features of WBC. Our proposed network framework achieves 98.77%, 91.30%, 98.36%, 99.71%, 98.45%, and 98.85% overall accuracy on the six public datasets.
  arXiv  Detail & Related papers  (2024-05-25T13:09:25Z)
• Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification [1.516937009186805]
  HemaX is an explainable deep neural network-based model that produces pathologist-like explanations using five attributes. HemaX achieves impressive results, with an average classification accuracy of 81.08% and a Jaccard index of 89.16% for cell localization.
  arXiv  Detail & Related papers  (2023-10-20T04:59:20Z)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells [6.117084972237769]
  Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells.
  arXiv  Detail & Related papers  (2023-03-07T14:26:08Z)
• 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)
• 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)
• BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images [69.41441138140895]
  This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
  arXiv  Detail & Related papers  (2021-10-05T19:14:46Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Weakly supervised multiple instance learning histopathological tumor segmentation [51.085268272912415]
  We propose a weakly supervised framework for whole slide imaging segmentation. We exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
  arXiv  Detail & Related papers  (2020-04-10T13:12:47Z)

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.