Haiyang Chen

Jiashun Suo, Tianyi Wang, Xingzhou Zhang et al.

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios including schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, such as flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://github.com/suojiashun/HIT-UAV-Infrared-Thermal-Dataset.

Haiyang Chen, Juan Gao, Zhuo Chen et al.

Background: Current mainstream cardiovascular magnetic resonance-feature tracking (CMR-FT) methods, including optical flow and pairwise registration, often suffer from the drift effect caused by accumulative tracking errors. Here, we developed a CMR-FT method based on deformable groupwise registration with a locally low-rank (LLR) dissimilarity metric to improve myocardial tracking and strain estimation accuracy. Methods: The proposed method, Groupwise-LLR, performs feature tracking by iteratively updating the entire displacement field across all cardiac phases to minimize the sum of the patchwise signal ranks of the deformed movie. The method was compared with alternative CMR-FT methods including the Farneback optical flow, a sequentially pairwise registration method, and a global low rankness-based groupwise registration method via a simulated dataset (n = 20), a public cine data set (n = 100), and an in-house tagging-MRI patient dataset (n = 16). The proposed method was also compared with two general groupwise registration methods, nD+t B-Splines and pTVreg, in simulations and in vivo tracking. Results: On the simulated dataset, Groupwise-LLR achieved the lowest point tracking errors and voxelwise/global strain errors. On the public dataset, Groupwise-LLR achieved the lowest contour tracking errors, reduced the drift effect in late-diastole, and preserved similar inter-observer reproducibility as the alternative methods. On the patient dataset, Groupwise-LLR correlated better with tagging-MRI for radial strains than the other CMR-FT methods in multiple myocardial segments and levels. Conclusions: The proposed Groupwise-LLR reduces the drift effect and provides more accurate myocardial tracking and strain estimation than the alternative methods. The method may thus facilitate a more accurate estimation of myocardial strains for clinical assessments of cardiac function.

Haiyang Chen, Hyung Jin Chang, Andrew Howes

Recent work in the behavioural sciences has begun to overturn the long-held belief that human decision making is irrational, suboptimal and subject to biases. This turn to the rational suggests that human decision making may be a better source of ideas for constraining how machine learning problems are defined than would otherwise be the case. One promising idea concerns human decision making that is dependent on apparently irrelevant aspects of the choice context. Previous work has shown that by taking into account choice context and making relational observations, people can maximize expected value. Other work has shown that Partially observable Markov decision processes (POMDPs) are a useful way to formulate human-like decision problems. Here, we propose a novel POMDP model for contextual choice tasks and show that, despite the apparent irrationalities, a reinforcement learner can take advantage of the way that humans make decisions. We suggest that human irrationalities may offer a productive source of inspiration for improving the design of AI architectures and machine learning methods.