Machine learning based lens-free imaging technique for field-portable cytometry

• URL: http://arxiv.org/abs/2203.00899v2
• Date: Thu, 3 Mar 2022 03:58:25 GMT
• Title: Machine learning based lens-free imaging technique for field-portable cytometry
• Authors: Rajkumar Vaghashiya, Sanghoon Shin, Varun Chauhan, Kaushal Kapadiya, Smit Sanghavi, Sungkyu Seo, Mohendra Roy
• Abstract summary: The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types. The model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Lens-free Shadow Imaging Technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been evolved, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the hand-crafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance is suffering from poor image (cell diffraction pattern) signatures due to its small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as Red Blood Cell (RBC) and White Blood Cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.

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)
• Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations [15.920475243253765]
  This study comprehensively evaluates the performance of cell instance segmentation models under simulated aberration conditions. Various segmentation models, such as Mask R-CNN with different network heads, were trained and tested under aberrated conditions. Results indicate that FPN combined with SwinS demonstrates superior robustness in handling simple cell images affected by minor aberrations.
  arXiv  Detail & Related papers  (2024-04-12T15:45:26Z)
• Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images [40.347953893940044]
  We introduce a novel approach based on neural cellular automata (NCA) for white blood cell classification. Our NCA-based method is significantly smaller in terms of parameters and exhibits to domain shifts. Results demonstrate that NCA can be used for image classification, but also 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)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Interpretable Single-Cell Set Classification with Kernel Mean Embeddings [14.686560033030101]
  Kernel Mean Embedding encodes the cellular landscape of each profiled biological sample. We train a simple linear classifier and achieve state-of-the-art classification accuracy on 3 flow and mass datasets.
  arXiv  Detail & Related papers  (2022-01-18T21:40:36Z)
• Analysis of Vision-based Abnormal Red Blood Cell Classification [1.6050172226234583]
  Identification of abnormalities in red blood cells (RBC) is key to diagnosing a range of medical conditions from anaemia to liver disease. This paper presents an automated process utilising the advantages of machine learning to increase capacity and standardisation of cell abnormality detection.
  arXiv  Detail & Related papers  (2021-06-01T10:52:41Z)
• Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare [77.99636165307996]
  We classify biomedical images using ensembles of neural networks. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish, Mish, Mexican Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign.
  arXiv  Detail & Related papers  (2020-11-24T01:53:39Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform [0.5599792629509229]
  We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images. The method utilizes multiple markers stained in situ on a single tissue section on a robust hyperplex immunofluorescence platform.
  arXiv  Detail & Related papers  (2020-07-18T16:45:32Z)

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.