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

Abstract

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.

RP-Net: A PointNet++ 3D Face Recognition Algorithm Integrating RoPS Local Descriptor

“…In order to compare our recognition results with the latest performance implemented on the Bosphorus dataset, we give the recognition rate of ranking 1 of the existing algorithms in Table 3. It can be seen that koushik Dutta et al [33] achieved the best recognition results in the normal environment, and the rank-1 recognition rate was 98.54%. The rank-1 recognition rate of RP-Net algorithm is 98.0%; In the weak light environment, RP-Net is 0.7% higher than the algorithm proposed by Koushik Dutta et al…”