RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification

Basha, S. H. Shabbeer; Ghosh, Soumen; Babu, K. K. Anish; Dubey, Shiv Ram; Pulabaigari, Viswanath; Mukherjee, Snehasis

doi:10.1109/icarcv.2018.8581147

Cited by 30 publications

(5 citation statements)

References 29 publications

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“…However, the handcrafted features limit the representation capabilities of nuclei entities. Recently, the nuclei classification models usually infer cell types based on the CNNs for nucleus segmentation (Zhang et al 2017;Basha et al 2018;Lou et al 2022Lou et al , 2023bMa et al 2023;Yu et al 2023) or nucleus centroid detection (Abousamra et al 2021;Huang et al 2023b). Graham et al (2019) propose a CNN of three branches, predicting nucleus types for the segmented nucleus instances.…”

Section: Related Workmentioning

confidence: 99%

Cell Graph Transformer for Nuclei Classification

Lou,

Li,

Wan

et al. 2024

AAAI

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Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fixed edges. To address the issue, we develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Nevertheless, training the transformer with a cell graph presents another challenge. Poorly initialized features can lead to noisy self-attention scores and inferior convergence, particularly when processing the cell graphs with numerous connections. Thus, we further propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor. The pre-trained features may suppress unreasonable correlations and hence ease the finetuning of CGT. Experimental results suggest that the proposed cell graph transformer with topology-aware pretraining significantly improves the nuclei classification results, and achieves the state-of-the-art performance. Code and models are available at https://github.com/lhaof/CGT

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Section: Related Workmentioning

confidence: 99%

Cell Graph Transformer for Nuclei Classification

Lou,

Li,

Wan

et al. 2024

AAAI

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“…Among these models, the convolutional neural network (CNN) [14] has gained popularity due to its superior ability to classify features compared to manually designed ones. That's strategy applied in various image processing tasks, including image classification [15,16], object detection [17], semantic segmentation [18], colon cancer classification [19], depth estimation [20], face anti-spoofing [21], and related domains. Advanced techniques within the field of deep learning have been suggested for dealing with the problem of shifting domains in hyperspectral imaging (HSI).…”

Section: Introductionmentioning

confidence: 99%

Attention 3D central difference convolutional dense network for hyperspectral image classification

Ashraf,

Alharthi,

Chen

et al. 2024

PLoS ONE

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Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

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“…The experimental results demonstrated that pre-trained VGG16 models utilizing transfer learning (i.e., the second and third VGG16 models) outperformed the supervised learning approach of the VGG16 which was fully trained from scratch. Basha et al [11] developed a CNN called RCCNet to classify colon cancer nuclei into four categories: miscellaneous, fibroblast, epithelial, and inflammatory. Their developed model was compared with various DL models: WRN, GoogLeNet, AlexNet, softmaxCNN, and softmaxCNN_IN27, and their proposed model achieved the best performance results.…”

Section: Introductionmentioning

confidence: 99%

“…The problem with detecting colon cancer using medical images depends on the datadriven methods used and the images generated by an imaging modality. Unlike previous studies that have focused on evaluating the performance behavior of DL models in terms of detecting colon cancer [8][9][10][11][12], our contributions can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection

Alhazmi,

Turki

2023

Diagnostics

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The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. Next, we assessed the DL models on the NVIDIA GeForce RTX 3080 Laptop GPU (16GB GDDR6 VRAM) using 5400 processed images divided equally between normal colons and colons with cancer for each of the imaging modalities used. Comparing the imaging modalities when applied to the five DL models presented in this study and twenty-six ensemble DL models, the experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model under the TL setting, outperforms all the other models by generating the highest average performance result of 99.1% (99.1%, 99.8%, and 99.1%) based on the accuracy results (AUC, precision, and F1, respectively).

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RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification

Cited by 30 publications

References 29 publications

Cell Graph Transformer for Nuclei Classification

Cell Graph Transformer for Nuclei Classification

Attention 3D central difference convolutional dense network for hyperspectral image classification

Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection

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