Towards Interpretable Classification of Leukocytes based on Deep Learning

• URL: http://arxiv.org/abs/2311.14485v1
• Date: Fri, 24 Nov 2023 13:48:37 GMT
• Title: Towards Interpretable Classification of Leukocytes based on Deep Learning
• Authors: Stefan R\"ohrl and Johannes Groll and Manuel Lengl and Simon Schumann and Christian Klenk and Dominik Heim and Martin Knopp and Oliver Hayden and Klaus Diepold
• Abstract summary: This work investigates the calibration of confidence estimation for the automated classification of leukocytes. In addition, different visual explanation approaches are compared, which should bring machine decision making closer to professional healthcare applications.
• Score: 0.7227323884094953
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Label-free approaches are attractive in cytological imaging due to their flexibility and cost efficiency. They are supported by machine learning methods, which, despite the lack of labeling and the associated lower contrast, can classify cells with high accuracy where the human observer has little chance to discriminate cells. In order to better integrate these workflows into the clinical decision making process, this work investigates the calibration of confidence estimation for the automated classification of leukocytes. In addition, different visual explanation approaches are compared, which should bring machine decision making closer to professional healthcare applications. Furthermore, we were able to identify general detection patterns in neural networks and demonstrate the utility of the presented approaches in different scenarios of blood cell analysis.

Related papers

• 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)
• 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)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy [43.55994393060723]
  gaitXplorer is a visual analytics approach for the classification of CP-related gait patterns. It integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications.
  arXiv  Detail & Related papers  (2022-08-10T09:21:28Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Discriminative Attribution from Counterfactuals [64.94009515033984]
  We present a method for neural network interpretability by combining feature attribution with counterfactual explanations. We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
  arXiv  Detail & Related papers  (2021-09-28T00:53:34Z)
• An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images [3.33281597371121]
  We describe an automated yet interpretable system for uveal melanoma subtyping with digital images from fine needle aspiration biopsies. Our method embeds every automatically segmented cell of a candidate image as a point in a 2D manifold defined by many representative slides. A rule-based slide-level classification algorithm is trained on the partitions of the circularly distorted 2D manifold.
  arXiv  Detail & Related papers  (2021-08-13T13:55:08Z)
• Towards Interpretable Attention Networks for Cervical Cancer Analysis [24.916577293892182]
  We evaluate various state-of-the-art deep learning models for the classification of images of multiple cervical cells. We show the effectiveness of the residual channel attention model for extracting important features from a group of cells. It also provides interpretable models to address the classification of cervical cells.
  arXiv  Detail & Related papers  (2021-05-27T13:28:24Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers [0.0]
  Unsupervised machine learning methodology is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence (RT-FDC) Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells.
  arXiv  Detail & Related papers  (2020-03-02T15:11:46Z)

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.