Cell nuclei classification in histopathological images using hybrid OLConvNet

• URL: http://arxiv.org/abs/2202.10177v1
• Date: Mon, 21 Feb 2022 12:39:37 GMT
• Title: Cell nuclei classification in histopathological images using hybrid OLConvNet
• Authors: Suvidha Tripathi and Satish Kumar Singh
• Abstract summary: We have proposed a hybrid and flexible deep learning architecture OLConvNet. $CNN_3L$ reduces the training time by training fewer parameters. We observed that our proposed model works well and perform better than contemporary complex algorithms.
• Score: 13.858624044986815
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges in developing state of the art algorithms due to the heterogeneity of cell nuclei and data set variability. Recently, a multitude of classification algorithms has used complex deep learning models for their dataset. However, most of these methods are rigid and their architectural arrangement suffers from inflexibility and non-interpretability. In this research article, we have proposed a hybrid and flexible deep learning architecture OLConvNet that integrates the interpretability of traditional object-level features and generalization of deep learning features by using a shallower Convolutional Neural Network (CNN) named as $CNN_{3L}$. $CNN_{3L}$ reduces the training time by training fewer parameters and hence eliminating space constraints imposed by deeper algorithms. We used F1-score and multiclass Area Under the Curve (AUC) performance parameters to compare the results. To further strengthen the viability of our architectural approach, we tested our proposed methodology with state of the art deep learning architectures AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 as backbone networks. After a comprehensive analysis of classification results from all four architectures, we observed that our proposed model works well and perform better than contemporary complex algorithms.

Related papers

• Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels [56.69755544814834]
  Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures. This paper reveals another striking property of DS-CNN architectures: discernible and explainable patterns emerge in their trained depthwise convolutional kernels in all layers.
  arXiv  Detail & Related papers  (2024-01-25T19:05:53Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search [104.45426861115972]
  We propose to directly generate structural parameters by utilizing the specifically designed hyper kernels. We obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. A series of experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-04-23T17:27:40Z)
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction [62.691996239590125]
  We propose an adaptive convolutional dictionary network (ACDNet) for metal artifact reduction. Our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image. Our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods.
  arXiv  Detail & Related papers  (2022-05-16T06:49:36Z)
• End-to-end Neuron Instance Segmentation based on Weakly Supervised Efficient UNet and Morphological Post-processing [0.0]
  We present an end-to-end weakly-supervised framework to automatically detect and segment NeuN stained neuronal cells on histological images. We integrate the state-of-the-art network, EfficientNet, into our U-Net-like architecture.
  arXiv  Detail & Related papers  (2022-02-17T14:35:45Z)
• Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution [80.93870349019332]
  We propose a novel HSISR method that uses Transformer instead of CNN to learn the prior of HSIs. Specifically, we first use the gradient algorithm to solve the HSISR model, and then use an unfolding network to simulate the iterative solution processes.
  arXiv  Detail & Related papers  (2021-11-27T15:38:57Z)
• Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification [1.4806818833792859]
  We propose to use a statistical network analysis method to describe the complex structure of the tissue micro-environment. We show that by analysing only the interactions between the cells in a network, we can extract highly discriminative statistical features for CRA grading. We create cell networks on a broad CRC histology image dataset, experiment with our method, and report state-of-the-art performance for the prediction of three-class CRA grading.
  arXiv  Detail & Related papers  (2021-06-29T12:22:10Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Differentiable Neural Architecture Learning for Efficient Neural Network Design [31.23038136038325]
  We introduce a novel emph architecture parameterisation based on scaled sigmoid function. We then propose a general emphiable Neural Architecture Learning (DNAL) method to optimize the neural architecture without the need to evaluate candidate neural networks.
  arXiv  Detail & Related papers  (2021-03-03T02:03:08Z)
• Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm [2.6629444004809826]
  Genetic U-Net is proposed to generate a U-shaped convolutional neural network (CNN) that can achieve better retinal vessel segmentation but with fewer architecture-based parameters. The experimental results show that the architecture obtained using the proposed method offered a superior performance with less than 1% of the number of the original U-Net parameters in particular.
  arXiv  Detail & Related papers  (2020-10-29T13:31:36Z)
• A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network [11.040847116812046]
  We introduce a genetic algorithm-based technique to reduce the efforts of finding the optimal combination of a hyper-parameter ( Kernel size) of a convolutional neural network-based architecture. The method is evaluated on three popular datasets of different handwritten Bangla characters and digits.
  arXiv  Detail & Related papers  (2019-12-28T05:37:28Z)

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.