Achieve Realtime Object Detection for 4K and 8K Endoscopes with FPGA

"Endoscopes have been successfully and widely used to in varies diagnoses and procedures. The National Polyp Study showed that 70%–90% of Colorectal cancer are preventable with regular colonoscopies and removal of polyps. It is estimated that 85% of these “interval cancers” are due to missed polyps or incompletely removed polyps during colonoscopy. These misses come from both equipment factors and human errors. Most of existing colonoscopes are based on either 1080p HD image or 2K imaging technology. With such a resolution, images are blurred in many cases, which makes it impossible to distinguish diverse tissue architectures which appear similar to each other and are easily confused. Recent development in UHD endoscopic system leads to much better image quality, which are likely to reduce miss diagnoses. With UHD endoscopes, another challenge to address to reduce miss diagnoses is the human factor. While doctors will gain more experiences and knowledge as they practice and learn, deep learning algorithms have shown great success. However, existing algorithms can only process 3-6 fps on 4K videos and 2fps on 8K videos, which are far away from the real-time requirement (25-30 fps) for endoscopy applications. The goal of this project is to develop a high performance accurate CNN accelerator for 4K and 8K videos to realize real time object detection and evaluate the performance with FPGA implementation.

Cognitive Biases in Understanding the Influence of Shale Gas Exploitation: From Environmental and Economic Perspectives

People have higher expectations for shale gas exploitation. However, the promotion of large-scale shale gas exploitation does not seem to be as good as it seems, since the extraction technology - hydraulic fracturing - harms the environment, which causes cognitive biases. This paper reviews studies that estimate the environmental and economic influence of the fracturing process in the U.S. and China to help people better understand the shale gas exploitation. It summarizes the methodological issues and results of main projections. There are shared problems in evaluating the influence of shale gas development due to limited identification methods, data sources and advancing exploitation technologies. Little research values the environmental influence of shale gas development in social benefit or economic benefit. Though varies significantly across various plays and parameter compared with conventional gases, previous researches indicate that water use for shale gas development will not affect the local water supply vastly, and the ultimate influence relies on the water management method. Moreover, compared with conventional natural gas and other energy resources, freshwater consumption about shale gas exploration is decreasing with the progress of exploration technology, while its life-cycle GHG emissions are greater in the long term

Additional Positive Enables Better Representation Learning for Medical Images

This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batch-wise per-sample gradient computation method to estimate the pairwise TracIn to represent the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.Comment: 8 page