List of Titles

Circumferential traversal techniques for characterizing shapes in digital images

- Maeder, Anthony

Authors: Maeder, Anthony
Date: 1997
Type: Text , Conference proceedings
Full Text: false
Description: Characterizing of region shapes in digital images is a common requirement in medical image processing. This paper describes an approach based on successive traversals around the region boundary, enabling a sequence of related shape information at different scales to be constructed. The approach is useful in that it allows several different shape characteristics to be determined using the same set of data. The approach and its implementation is described, and an example of its application to a problem in bio-medical cell discrimination is considered and compared with results from more conventional shape characterization techniques.
Description: E1

Collaborative SDOCT segmentation and analysis software

- Yun, Yeyi, Carass, Aaron, Lang, Andrew, Prince, Jerry, Antony, Bhavna

Authors: Yun, Yeyi , Carass, Aaron , Lang, Andrew , Prince, Jerry , Antony, Bhavna
Type: Text , Conference proceedings
Relation: SPIE Medical Imaging:Imaging Informatics for Healthcare, Research. and Applications, 2017; Florida, United States Vol. 10138
Full Text: false
Reviewed:
Description: Spectral domain optical coherence tomography (SDOCT) is routinely used in the management and diagnosis of a variety of ocular diseases. This imaging modality also finds widespread use in research, where quantitative measurements obtained from the images are used to track disease progression. In recent years, the number of available scanners and imaging protocols grown and there is a distinct absence of a unified tool that is capable of visualizing, segmenting, and analyzing the data. This is especially noteworthy in longitudinal studies, where data from older scanners and/or protocols may need to be analyzed. Here, we present a graphical user interface (GUI) that allows users to visualize and analyze SDOCT images obtained from two commonly used scanners. The retinal surfaces in the scans can be segmented using a previously described method, and the retinal layer thicknesses can be compared to a normative database. If necessary, the segmented surfaces can also be corrected and the changes applied. The interface also allows users to import and export retinal layer thickness data to an SQL database, thereby allowing for the collation of data from a number of collaborating sites.

Memory-Based explainable reinforcement learning

Type: Conference proceedings
Relation: 32nd Australasian Joint Conference on Artificial Intelligence AI , Switzerland, 2019 in AI 2019 ; Advances in artificial intelligence ; published in Lecture Notes in Computer Science, vol 11919 , pp.66-77
Full Text: false
Description: Reinforcement learning (RL) is a learning approach based on behavioral psychology used by artificial agents to learn autonomously by interacting with their environment. An open issue in RL is the lack of visibility and understanding for end-users in terms of decisions taken by an agent during the learning process. One way to overcome this issue is to endow the agent with the ability to explain in simple terms why a particular action is taken in a particular situation. In this work, we propose a memory-based explainable reinforcement learning (MXRL) approach. Using an episodic memory, the RL agent is able to explain its decisions by using the probability of success and the number of transactions to reach the goal state. We have performed experiments considering two variations of a simulated scenario, namely, an unbounded grid world with aversive regions and a bounded grid world. The obtained results show that the agent, using information extracted from the memory, is able to explain its behavior in an understandable manner for non-expert end-users at any moment during its operation

Team recognition in big scholarly data: exploring collaboration intensity

- Yu, Shuo, Xia, Feng, Zhang, Kaiyuan, Ning, Zhaolong, Zhong, Jiaofei, Liu, Chengfei

Authors: Yu, Shuo , Xia, Feng , Zhang, Kaiyuan , Ning, Zhaolong , Zhong, Jiaofei , Liu, Chengfei
Type: Text , Conference proceedings
Relation: p. 925-932
Full Text: false
Reviewed:
Description: The scale of scholarly data has been expanded due to the fact that scientific productions are increasing rapidly and new scholars affiliate academia incessantly. Scholars are shifting their research patterns from individual research to academic teamwork due to the complexity of scientific issues. In order to achieve higher reputations and better performance, academic teams with leaders are constructed to speed up knowledge sharing and problem solving. It is significant to explore team-based issues with the increasing interests of information exploration in big scholarly data. However, existing academic team definitions are commonly not quantitative, which makes it difficult to identify real academic teams. In this work, we propose a collaboration relationship evaluation index called Collaboration Intensity Index (CII), which is a two-way and quantitative index to evaluate collaboration intensity between two scholars in the network. Then, we construct a new type of co-author network with edges weighted by CII, which differs from the original co-author networks. This network reflects the newly scientific research patterns inside or outside academic teams. Furthermore, we propose TRAC (Team Recognition Algorithm based on CII) to identify academic teams from large co-author networks. Finally, we use DBLP data set, which contains 1,250,440 scholars and 1,575,949 published papers, to identify teams by TRAC. Comparing with fast unfolding algorithm and real team data, the effectiveness of our method can be demonstrated.

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