Analysis–Synthesis Learning With Shared Features: Algorithms for Histology Image Classification

Li, Xuelu; Monga, Vishal; Rao, U. K. Arvind

doi:10.1109/tbme.2019.2928997

Cited by 12 publications

(18 citation statements)

References 28 publications

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“…In this section we evaluate our proposed GS-PCANet image classification algorithm with other open-source histopathology image classification methods: SpPCANet method for image classification [46], multiple clustered instance learning (MCIL) for histopathology image classification [50], saliency-based dictionary learning (SDL) [34], analysis-synthesis learning with shared features (ASLF) [35], patch-based convolutional neural network (PCNN) [36], encoded local projections (ELP) for histopathology image classification [20], and weakly supervised deep learning (WSDL) for whole slide tissue classification [40]. We evaluate these seven methods using commonly used detection/classification measures: precision (P), recall (R), detection accuracy, F β -score, Tanimoto coefficient (T), and the receiver operating characteristic (ROC) curves along with the area under the curve (AUC).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…These features are then used to classify an image patch as cancerous or healthy using a linear SVM classifier. Our approach differs from earlier learning-based methods based on deep learning [36], [40], instance learning [20], [50] or dictionary learning [34], [35] for histopathology image classification. Like many deep learning methods, the network parameters, such as the number of stages, the filter size, and the number of filters, need to be optimized and fixed for our GS-PCANet method.…”

Section: Discussionmentioning

confidence: 99%

“…One such popular class of learning or feature discovery methods includes those based on sparse representationbased classification (SRC) [31]. There have been many SRC methods that have been successfully applied to a variety of histopathological image classification problems [32]- [35]. These methods are based on finding linear representations in the data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Ram¹,

Tang²,

Bell³

et al. 2021

Preprint

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Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-ras G12D lung cancer mouse model using precision/recall rates, F β -score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Ram¹,

Tang²,

Bell³

et al. 2021

Preprint

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“…This approach was employed in an image analysis and data mining pipeline to identify histological features capable of differentiating between cancer and non-cancer lesions and the malignant transformation-potential in gliomas (figure 2). 52 Using whole slide imaging data from the Cancer Genome Atlas and companion clinical data for these specimens, we assessed the prognostic relevance of these histological discriminants 53 54. Histopathology image-derived measurements, such as cell morphologies, spatial patterns of cellular organisation, in combination with a bag-of-words (BoW) approach53 55 was used to identify tissue subregions that have visually distinct properties (eg, nuclear morphology, patterns of spatial organisation) and were associated with time-to-malignant transformation.…”

Section: Ai and ML Approachesmentioning

confidence: 99%

Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa

Waljee

Weinheimer-Haus

Abubakar

et al. 2022

Gut

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“…new revenues of computational pathology. There have been some computational pathology tools that support pathologists for very routine tasks such as to segment nuclei [3]- [5] or tumour [6] and to classify cancer in histopathological images [7]- [9]. Due to the promising impact on future pathology practice, digital pathology and computational pathology have been attracting tremendous attention [10], [11].…”

Section: Introductionmentioning

confidence: 99%

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

Sun

Huang

Zhou

et al. 2021

Preprint

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The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customized convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve stateof-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines. The sourcecode is publicly available at: https://github.com/sigma10010/nuclei cells det.

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Analysis–Synthesis Learning With Shared Features: Algorithms for Histology Image Classification

Cited by 12 publications

References 28 publications

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

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