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In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation. ... To alleviate the burden of manual annotation, in this paper, we propose a new weakly supervised DL approach for biomedical image segmentation using boxes only annotation. ... We use 7 of them for training and the rest of them for testing. One might wonder whether 7 training images are too few to train our deep learning model. ...arXiv:1806.00593v1 fatcat:hxyrku6qcfc4vljkjwikjp34du
We hope that this survey will help accelerate the use of deep learning across different scientific domains. ... training methods, along with techniques to use deep learning with less data and better interpret these complex models --- two central considerations for many scientific use cases. ... Deep learning techniques, which are capable of many complex transformations of data, can be highly effective for such settings, for example, using a deep neural network based segmentation model to automatically ...arXiv:2003.11755v1 fatcat:igy35ko5hfcj5ctp5nck7y2z44
Having first overviewed some of the key deep learning models, algorithms and use cases, we begin by introducing quantitative techniques that can give insights into neural network hidden representations ... Over the past several years, we have witnessed fundamental breakthroughs in machine learning, largely driven by rapid advancements of the underlying deep neural network models and algorithms. ... Deep learning techniques, which are capable of many complex transformations of data, can be highly effective for such settings, for example, using a deep neural network based segmentation model to automatically ...doi:10.7298/xvk2-m314 fatcat:f3qjq56xyrdbpognytmc6oizsu