Deep Generative Classification of Blood Cell Morphology

• URL: http://arxiv.org/abs/2408.08982v1
• Date: Fri, 16 Aug 2024 19:17:02 GMT
• Title: Deep Generative Classification of Blood Cell Morphology
• Authors: Simon Deltadahl, Julian Gilbey, Christine Van Laer, Nancy Boeckx, Mathie Leers, Tanya Freeman, Laura Aiken, Timothy Farren, Matthew Smith, Mohamad Zeina, BloodCounts! consortium, Concetta Piazzese, Joseph Taylor, Nicholas Gleadall, Carola-Bibiane Schönlieb, Suthesh Sivapalaratnam, Michael Roberts, Parashkev Nachev,
• Abstract summary: We introduce CytoDiffusion, a diffusion-based classifier that effectively models blood cell morphology. Our approach outperforms state-of-the-art discriminative models in anomaly detection. We enhance model explainability through the generation of directly interpretable counterfactual heatmaps.
• Score: 7.611536156732807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of cell morphology, heterogeneities of biological, pathological, and imaging characteristics, and the imbalance of cell type frequencies. We introduce CytoDiffusion, a diffusion-based classifier that effectively models blood cell morphology, combining accurate classification with robust anomaly detection, resistance to distributional shifts, interpretability, data efficiency, and superhuman uncertainty quantification. Our approach outperforms state-of-the-art discriminative models in anomaly detection (AUC 0.976 vs. 0.919), resistance to domain shifts (85.85% vs. 74.38% balanced accuracy), and performance in low-data regimes (95.88% vs. 94.95% balanced accuracy). Notably, our model generates synthetic blood cell images that are nearly indistinguishable from real images, as demonstrated by a Turing test in which expert haematologists achieved only 52.3% accuracy (95% CI: [50.5%, 54.2%]). Furthermore, we enhance model explainability through the generation of directly interpretable counterfactual heatmaps. Our comprehensive evaluation framework, encompassing these multiple performance dimensions, establishes a new benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings. Our code is available at https://github.com/Deltadahl/CytoDiffusion.

Related papers

• Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations [1.5146068448101746]
  Abnormalities in the gastrointestinal tract significantly influence the patient's health and require a timely diagnosis. The work presents the process of developing and evaluating a novel model designed to classify gastrointestinal anomalies from a video frame. integration of Omni Dimensional Gated Attention (OGA) mechanism and Wavelet transformation techniques into the model's architecture allowed the model to focus on the most critical areas. The model's performance is benchmarked against two base models, VGG16 and ResNet50, demonstrating its enhanced ability to identify and classify a range of gastrointestinal abnormalities accurately.
  arXiv  Detail & Related papers  (2024-10-25T08:01:35Z)
• Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model [1.0994755279455526]
  This study proposes a hybrid model that combines the advantages of Transformers and Convolutional Neural Networks (CNNs) to enhance classification performance. For the GastroVision dataset, our proposed model demonstrates excellent performance with Precision, Recall, F1 score, Accuracy, and Matthews Correlation Coefficient (MCC) of 0.8320, 0.8386, 0.8324, 0.8386, and 0.8191, respectively.
  arXiv  Detail & Related papers  (2024-08-20T11:05:32Z)
• 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)
• BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body [0.0]
  Blood cell classification and counting is vital for the diagnosis of various blood-related diseases. We have proposed a DL based automated system for blood cell classification and counting from microscopic blood smear images. We classify total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet.
  arXiv  Detail & Related papers  (2024-04-01T20:38:58Z)
• 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)
• Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis [42.624671531003166]
  We introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks.
  arXiv  Detail & Related papers  (2022-08-19T12:06:46Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Deep CNNs for Peripheral Blood Cell Classification [0.0]
  We benchmark 27 popular deep convolutional neural network architectures on the microscopic peripheral blood cell images dataset. We fine-tune the state-of-the-art image classification models pre-trained on the ImageNet dataset for blood cell classification.
  arXiv  Detail & Related papers  (2021-10-18T17:56:07Z)
• 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)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)

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.