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Tuberculosis detection using few shot learning [PDF]
Scientific ReportsTuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good results
Kamran Riasat +5 more
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Federated knee injury diagnosis using few shot learning [PDF]
Frontiers in Artificial IntelligenceIntroductionKnee injuries, especially Anterior Cruciate Ligament (ACL) tears and meniscus tears, are becoming increasingly common and can severely restrict mobility and quality of life.
Chirag Goel, Anita X, Jani Anbarasi L
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Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning
Frontiers in Neuroscience, 2022 Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks.
Shuangming Yang +2 more
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MetaChest: generalized few-shot learning of pathologies from chest X-rays [PDF]
Visual Computing for Industry, Biomedicine, and ArtThe limited availability of annotated data presents a major challenge in applying deep learning methods to medical image analysis. Few-shot learning methods aim to recognize new classes from only a few labeled examples.
Berenice Montalvo-Lezama +1 more
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A novel framework GRCornShot for corn disease detection using few shot learning with prototypical network [PDF]
Scientific ReportsPrecision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods.
Ruchi Rani +3 more
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Survey on Machine Learning Methods for Small Sample Data [PDF]
Jisuanji gongcheng, 2022 Few-shot learning is a type of machine learning method for small sample data that operates by using less supervised sample data to build machine learning models that can solve practical problems.Therefore, few-shot learning can be used to solve the ...
CHEN Liangchen, FU Deyin
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Multi-Similarity Enhancement Network for Few-Shot Segmentation
IEEE Access, 2023 Few-Shot Segmentation (FSS) is challenging for intra-class diversity and support sample scarcity. Many works focus on the class-wise or pixel-wise similarity between the support foreground and query sample while neglecting the support background, which ...
Hao Chen, Zhe-Ming Lu, Yang-Ming Zheng
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Filtering Specialized Change in a Few-Shot Setting
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The aim of change detection in remote sensing usually is not to find all differences between the observations, but rather only specific types of change, such as urban development, deforestation, or even more specialized categories like roadwork. However,
Martin Hermann +2 more
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HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification
International Journal of Applied Earth Observations and Geoinformation, 2023 Few-shot learning is an important and challenging research topic for remote sensing image scene classification. Many existing approaches address this challenge by using meta-learning and metric-learning techniques, which aim to develop feature extractors
Junjie Zhu +4 more
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Semantic Matching Network for Few-Shot Learning [PDF]
Jisuanji gongcheng, 2021 In the field of deep learning,it is difficult to achieve visual recognition with a small number of samples.To address the problem,this paper proposes a semantic matching network.The dual attention mechanism is used to match the semantic information of ...
WANG Ronggui, TANG Mingkong, YANG Juan, XUE Lixia, HU Min
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