Utilization of Whole Slide Pathological Images and Tumor Clinical Data: A Hybrid Graph Convolutional Network Model for Hepatocellular Carcinoma Prognosis Prediction.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, ranking fourth in cancer-related mortality. Prognostic risk prediction for HCC patients can optimize treatment strategies, assess therapeutic efficacy, and ultimately improve post-operative survival rates. Pathological slides are considered the gold standard for cancer diagnosis and prognosis, playing a crucial role in prognostic risk stratification.

Habitat fragmentation and biodiversity response: Controversy, attribution, and prospects.

As global ecosystems continue to degrade, the impact of habitat fragmentation on biodiversity has become a critical issue in biodiversity conservation. However, both negative and positive impacts of habitat fragmentation on biodiversity have been reported in different studies. The coexistence and exchange of opposing views has increasingly evolved into debates based on inherent positions, which seriously restricts the further research development and the theoretical guidance of biodiversity conservation.

A steel defect detection method based on edge feature extraction via the Sobel operator.

Scratches and cracks in steel severely affect its service life and performance. However, owing to the irregular shapes and sizes of steel surface defects, defects within the same class may be different, whereas defects between classes may be similar. Existing methods focus only on spatial information, resulting in low detection accuracy.

GCN-Based Risk Prediction for Necrosis Slide of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world which ranks fourth in cancer deaths. Primary pathological necrosis is an effective prognostic indicator for hepatocellular carcinoma. We propose a GCN-based approach that mimics the pathologist's perspective for global assessment of necrosis tissue distribution to analyze patient survival.

BlockQNN: Efficient Block-Wise Neural Network Architecture Generation.

Convolutional neural networks have gained a remarkable success in computer vision. However, most popular network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy.