Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

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-rasG12D lung cancer mouse model using precision/recall rates, Fß-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.

A dendrite-free Li plating host towards high utilization of Li metal anode in Li-O2 battery.

The intense interest of Li-O2 battery stems from its ultrahigh theoretical energy density, but its application is still hindered by the issues of Li anode. Herein, RuO2-CNTs composite, a conventional O2 cathode catalyst in Li-O2 battery, is first utilized as an anode host for dendrite-free Li plating/stripping with high Coulombic efficiency. It is demonstrated that such excellent plating/stripping performance arises from the lithiophilicity characteristic of Ru nanoparticles (that is derived from the in-situ electrochemical conversion from RuO2 to Ru/Li2O) and buffer space provided by CNTs. Furthermore, the RuO2-CNTs electrode pre-deposited with limited Li (RuO2-CNTs@Li anode) is coupled with a RuO2-CNTs catalytic cathode to form a Li-O2 full cell, which displays an extended cycle life with dramatically improved energy density. The achieved cell shows a high stability of 200 cycles with RuO2-CNTs@Li anode (1 mg Li) that sheds light on the efficient utilization of Li anode in Li-O2 batteries.