International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
Lateral control of a simulated vehicle in a simulated highway driving environment is explored. Three modules are used: A driving simulator, a visual preprocessor, and a neural network. Once trained, the networks contr...
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Lateral control of a simulated vehicle in a simulated highway driving environment is explored. Three modules are used: a driving simulator, a visual preprocessor, and a neural network. Once trained, the networks contr...
详细信息
Lateral control of a simulated vehicle in a simulated highway driving environment is explored. Three modules are used: a driving simulator, a visual preprocessor, and a neural network. Once trained, the networks control the trajectory of the vehicle by accessing a steering decision for implementation at each timestep in response to a visual encoding of an image generated at the previous timestep. The paper presents the development of the three system modules, the creation of training sets, and computational results. Neural network performances are gauged by a number of procedures. Excellent results are achieved for straight roads and curved roads under a variety of initial conditions on the vehicle.< >
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