We propose a Rough set-theoretic approach for solving the stochastic Multi-Armed Bandit (MAB) problems. The proposed approach is a modification to the Epsilon-greedy (ϵ-greedy) algorithm used to solve the stochastic m...
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Navigation capability is crucial for the functionality of living machines across various domains. Recently, two types of approaches have been pursued to address navigation tasks: reinforcement learning (RL) based meth...
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Robustly estimating a person’s orientation in various clothing and image styles is essential for implementing vision systems in real-world applications. In this task, the spatial arrangement of local parts can be a k...
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Transfer learning is a common practice that alleviates the need for extensive data to train neural networks. It is performed by pre-training a model using a source dataset and fine-tuning it for a target task. However...
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Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in m...
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Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive op...
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Scene Text Image Super-Resolution (STISR) plays a crucial role in enhancing text readability within natural scenes, impacting OCR systems, visual question answering, and image retrieval. Existing STISR methods often f...
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High resolution micro-ultrasound has demonstrated promise in real-time prostate cancer detection, with deep learning becoming a prominent tool for learning complex tissue properties reflected on ultrasound. However, a...
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ISBN:
(纸本)9783031732836;9783031732843
High resolution micro-ultrasound has demonstrated promise in real-time prostate cancer detection, with deep learning becoming a prominent tool for learning complex tissue properties reflected on ultrasound. However, a significant roadblock to real-world deployment remains, which prior works often overlook: model performance suffers when applied to data from different clinical centers due to variations in data distribution. This distribution shift significantly impacts the model's robustness, posing major challenge to clinical deployment. Domain adaptation and specifically its test-time adaption (TTA) variant offer a promising solution to address this challenge. In a setting designed to reflect real-world conditions, we compare existing methods to state-of-the-art TTA approaches adopted for cancer detection, demonstrating the lack of robustness to distribution shifts in the former. We then propose Diverse Ensemble Entropy Minimization (DEnEM), questioning the effectiveness of current TTA methods on ultrasound data. We show that these methods, although outperforming baselines, are suboptimal due to relying on neural networks output probabilities, which could be uncalibrated, or relying on data augmentation, which is not straightforward to define on ultrasound data. Our results show a significant improvement of 5% to 7% in AUROC over the existing methods and 3% to 5% over TTA methods, demonstrating the advantage of DEnEM in addressing distribution shift.
To negotiate the safety and liability concerns of Autonomous Vehicles (AVs), manufacturers desire AVs to conform to traffic laws and regulations with auto-reasoning capabilities and the ability to detect passengers’ ...
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