Integration for Training Transfer: Learning, Knowledge, Organizational Culture, and Technology

Abstract Although current advances for automated glioma lesion segmentation and volumetric measurements using deep learning have yielded high performance on newly-diagnosed patients, response assessment in neuro-oncology still relies on manually-drawn, cross-sectional areas of the tumor because these models do not generalize to patients in the post-treatment setting, where they are most needed in the clinic. Surgical resections, adjuvant treatment, or disease progression can alter the characteristics of these lesions on T2-weighted imaging, causing measures of segmentation accuracy, typically measured by Dice coefficients of overlap (DCs), to drop by ~15%. To improve the generalizability of T2-lesion segmentation to patients with glioma post-treatment, we evaluated the effects of: 1) training with different proportions of newly-diagnosed and treated gliomas, 2) applying transfer learning from pre- to post-treatment domains, and 3) incorporating a loss term that spatially weights the lesion boundaries with greater emphasis in training. Using 425 patients (208 newly-diagnosed, 217 post-Tx, with 25 treated patients withheld as a test set) and a top-performing model previously trained on newly-diagnosed gliomas, we found that DCs increased by 10% (to 0.84) then plateaued after including ~25% of post-treatment patients in training. Transfer learning (pre-training on newly-diagnosed and finetuning with post-treatment data) significantly improved Hausdorf distances (HDs), a measure more sensitive to changes at the lesion boundaries, by 17% after including 26% post-treatment images in training, while DCs remained similar. Although modifying our loss functions with boundary-weighted penalizations resulted in comparable DCs to using standard DC loss, HD measures were further reduced by 26%, suggesting that HDs may be a more sensitive metric to subtle changes in segmentation accuracy than DCs. Current work is evaluating their utility in providing accurate volumes for real-time response assessment in the clinic using workflows that have recently been deployed on our clinical PACs system.

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Organizational culture for training transfer: the mediating role of motivation

International Journal of Organizational Analysis ◽

10.1108/ijoa-04-2020-2147 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Dhruba Kumar Gautam ◽

Dinesh Basnet

Keyword(s):

Organizational Culture ◽

Social Cohesion ◽

Training Transfer ◽

Education Sector ◽

Mediating Effect ◽

Related Factors ◽

Content Type ◽

Transfer Training ◽

Motivation To Transfer ◽

And Training

Purpose The purpose of this study is to examine the mediating effect of motivation to transfer training in between five dimensions of organizational culture and training transfer. Design/methodology/approach This paper followed a positivist epistemology to understand the factors associated with training transfer. The descriptive and causal research design was used for data analysis. A proportionate stratified random sample of 150 faculties responded to a self-administered questionnaire. Hierarchical regression analyses were performed to examine the hypotheses. Findings The results show that motivation to transfer training partially mediates the relationship between the four dimensions of organizational culture (i.e. job challenge, communication, innovation and social cohesion) and training transfer. Further, the results also show the relation between trust and training transfer is fully mediated by the motivation to transfer training. Practical implications For better training transfer higher education sector ought to consider organizational related factors such as organizational culture, rather than only focusing on individual-related factors. The education sector would have strengthened each dimension of organizational culture to motivate the faculties for training transfer. Originality/value Organizational culture dimensions (job challenge, communication, trust, innovation and social cohesion) are the essential dimensions for training transfer which are less prioritized despite their importance.

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The moderating role of self-efficacy in the organizational culture-training transfer relationship

International Journal of Training and Development ◽

10.1111/j.1468-2419.2011.00396.x ◽

2012 ◽

Vol 16 (2) ◽

pp. 92-106 ◽

Cited By ~ 24

Author(s):

Maria Simosi

Keyword(s):

Organizational Culture ◽

Self Efficacy ◽

Training Transfer ◽

Moderating Role

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Bootstrap an End-to-End ASR System by Multilingual Training, Transfer Learning, Text-to-Text Mapping and Synthetic Audio

10.21437/interspeech.2021-198 ◽

2021 ◽

Cited By ~ 1

Author(s):

Manuel Giollo ◽

Deniz Gunceler ◽

Yulan Liu ◽

Daniel Willett

Keyword(s):

Transfer Learning ◽

Training Transfer ◽

End To End ◽

Asr System

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Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning

Sensors ◽

10.3390/s20082320 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2320 ◽

Cited By ~ 1

Author(s):

Ender Çetin ◽

Cristina Barrado ◽

Enric Pastor

Keyword(s):

Reinforcement Learning ◽

Transfer Learning ◽

Training Transfer ◽

The State ◽

Neighborhood Environment ◽

Depth Image ◽

Depth Images ◽

Stationary Obstacles ◽

Time Required ◽

Moving Obstacle

Counter-drone technology by using artificial intelligence (AI) is an emerging technology and it is rapidly developing. Considering the recent advances in AI, counter-drone systems with AI can be very accurate and efficient to fight against drones. The time required to engage with the target can be less than other methods based on human intervention, such as bringing down a malicious drone by a machine-gun. Also, AI can identify and classify the target with a high precision in order to prevent a false interdiction with the targeted object. We believe that counter-drone technology with AI will bring important advantages to the threats coming from some drones and will help the skies to become safer and more secure. In this study, a deep reinforcement learning (DRL) architecture is proposed to counter a drone with another drone, the learning drone, which will autonomously avoid all kind of obstacles inside a suburban neighborhood environment. The environment in a simulator that has stationary obstacles such as trees, cables, parked cars, and houses. In addition, another non-malicious third drone, acting as moving obstacle inside the environment was also included. In this way, the learning drone is trained to detect stationary and moving obstacles, and to counter and catch the target drone without crashing with any other obstacle inside the neighborhood. The learning drone has a front camera and it can capture continuously depth images. Every depth image is part of the state used in DRL architecture. There are also scalar state parameters such as velocities, distances to the target, distances to some defined geofences and track, and elevation angles. The state image and scalars are processed by a neural network that joints the two state parts into a unique flow. Moreover, transfer learning is tested by using the weights of the first full-trained model. With transfer learning, one of the best jump-starts achieved higher mean rewards (close to 35 more) at the beginning of training. Transfer learning also shows that the number of crashes during training can be reduced, with a total number of crashed episodes reduced by 65%, when all ground obstacles are included.

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