Overcoming Data Limitation in Medical Visual Question Answering

Nguyen, B.D.; Do, Thanh-Toan; Nguyen, Binh X.; Do, Tuong; Tjiputra, Erman; Tran, Quang D.

doi:10.1007/978-3-030-32251-9_57

Cited by 107 publications

(66 citation statements)

References 13 publications

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“…Slow progress in the medical field Medical image processing [54] Drug discovery [56] Cancer detection [57] Medical vision question answering [58] Skin lesion segmentation tasks [60] Predicting the specific behavior of molecules Ability to learn to weigh support samples Computational Intelligence and Neuroscience (DDA) method proposed by Sun and Saenko [67] and Rozantsev et al [68] and the hybrid heterogeneous transfer learning (HHTL) algorithm proposed by Zhou et al [69] based on deep learning.…”

Section: Intelligent Medicinementioning

confidence: 99%

“…In [ 57 ], MAML can be adapted to weakly supervised breast cancer detection tasks, and the order of the tasks is selected according to the course, rather than randomly selected. MAML is also combined with a denoising autoencoder for medical vision question answering [ 58 ], and learning to weigh support samples as done in [ 59 ] can be applied to pixel weighting to handle skin lesion segmentation tasks with noisy labels [ 60 ].…”

Section: Specific Analysis Of the Application Of Metalearning In The Field Of Artificial Intelligencementioning

confidence: 99%

“…At the same time, Nguyen et al [ 58 ] proposed a novel visual question answering (VQA) framework for medical care, which overcomes the limitations of labeled data. Moreover, by combining the advantages of an unsupervised denoising autoencoder (DAE), which can take advantage of a large number of untagged images, with the advantages of supervised metalearning, which can learn metaweights with limited tagged data to quickly adapt to VQA problems, a small sample set can be used to train the proposed framework.…”

Section: Specific Analysis Of the Application Of Metalearning In The Field Of Artificial Intelligencementioning

confidence: 99%

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[Retracted] Review on the Application of Metalearning in Artificial Intelligence

Zhang

Wang

et al. 2021

Computational Intelligence and Neuroscience

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In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. Through learning the information metaknowledge, the ability to autonomously judge and select the appropriate model can be formed, and the parameters can be adjusted independently to achieve further optimization. It is a novel method to solve big data problems in the current neural network model, and it adapts to the development trend of artificial intelligence. This article first briefly introduces the research process and basic theory of metalearning and discusses the differences between metalearning and machine learning and the research direction of metalearning in big data. Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.

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Section: Intelligent Medicinementioning

confidence: 99%

Section: Specific Analysis Of the Application Of Metalearning In The Field Of Artificial Intelligencementioning

confidence: 99%

Section: Specific Analysis Of the Application Of Metalearning In The Field Of Artificial Intelligencementioning

confidence: 99%

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[Retracted] Review on the Application of Metalearning in Artificial Intelligence

Zhang

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…In [275] MAML is adapted to weaklysupervised breast cancer detection tasks, and the order of tasks are selected according to a curriculum. MAML is also combined with denoising autoencoders to do medical visual question answering [276], while learning to weigh support samples [218] is adapted to pixel wise weighting for skin lesion segmentation tasks that have noisy labels [277].…”

Section: Emerging Topicsmentioning

confidence: 99%

Meta-Learning in Neural Networks: A Survey

Hospedales¹,

Antoniou²,

Micaelli³

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

859

371

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The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.

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“…This technology effectively improves the training style and training time, and has strong adaptability and robustness to the unknown scenarios. For example, Nguyen et al [34] used a combination of unsupervised denoising auto encoder (DAE) and supervised meta-learning in order to overcome the data limitation in medical visual Q&A with only a small sample set, and the performance of this method is better than the existing medical visual question answering method; Wang et al [35] proposed a metalearning face recognition (meta face recognition, MFR) method, and the experimental results proved that the method is optimal in cross-racial and cross-scene tests, which greatly improves the generalization performance of the model. Meta-learning also has broad application prospects in the field of robotics learning [36].…”

Section: A Related Work In Meta-learningmentioning

confidence: 99%

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Zhou

2021

IEEE Access

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Separating target speech from noisy signal is important for many realistic applications. Recently, deep neural network (DNN) has been widely used in speech enhancement (SE) and obtained prominent performance improvements. However, the current deep models require a large amount of training data to obtain a good performance. It is still challenging to construct an effective deep speech enhancement model with actual few training samples. At present, meta-learning has become the research focus of few-shot learning due to its capability of quickly process new tasks with few samples by the prior meta-knowledge, but there are very few works applying meta-learning on few-shot speech enhancement. In this paper, we propose a generic meta-learning framework Meta-SE which applies the U-Net as the metalearner, to tackle the few-shot speech enhancement problem. Meta-SE is trained and optimized with the changed speech enhancement tasks to obtain meta-knowledge, and towards better capability of fast and good generalizing to the new unseen noises with few training samples. The experiment results show that the proposed method not only outperforms the state-of-the-arts DNN-SE models under the few-shot conditions, but also learns a more general and flexible model for task adaption. INDEX TERMS Speech enhancement, single-channel, meta-learning, few-shot learning This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Overcoming Data Limitation in Medical Visual Question Answering

Cited by 107 publications

References 13 publications

[Retracted] Review on the Application of Metalearning in Artificial Intelligence

[Retracted] Review on the Application of Metalearning in Artificial Intelligence

Meta-Learning in Neural Networks: A Survey

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

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