Membrane segmentation via active learning with deep networks

Gaur, Utkarsh; Kourakis, Matthew J.; Newman-Smith, Erin; Smith, William C.; Manjunath, B.S.

doi:10.1109/icip.2016.7532697

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…There are other methods that do not require even a small number of initial hand‐labeled data. Gaur et al . started the selection process with a deep model trained on a similar domain.…”

Section: Expanding Datasets For Deep Learningmentioning

confidence: 99%

Deep learning in medical imaging and radiation therapy

et al. 2018

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The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

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“…There are other methods that do not require even a small number of initial hand‐labeled data. Gaur et al . started the selection process with a deep model trained on a similar domain.…”

Section: Expanding Datasets For Deep Learningmentioning

confidence: 99%

Deep learning in medical imaging and radiation therapy

et al. 2018

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“…In addition, DAL also has important applications in the area of medical image segmentation. For example, [48] proposes an AL-based transfer learning mechanism for medical image segmentation, which can effectively improve the image segmentation performance on a limited labeled dataset. [176] combines fully convolutional networks (FCN) and AL to create a DAL framework for biological image segmentation.…”

Section: Image Classification and Recognitionmentioning

confidence: 99%

A Survey of Deep Active Learning

Ren¹,

Xiao²,

Chang³

et al. 2020

Preprint

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Active learning (AL) attempts to maximize a model's performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely accorded the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.) Therefore, AL is gradually coming to receive the attention it is due.It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DAL from an application perspective. Finally, we discuss the confusion and problems associated with DAL and provide some possible development directions.CCS Concepts: • Computing methodologies → Machine learning algorithms.

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“…There are other methods that do not require even a small number of initial hand-labeled data. Gaur et al 343 started the selection process with a deep model trained on a similar domain. Then, they interpreted the active learning problem of increasing the size of limited labeled dataset as an optimization problem by maximizing both the uncertainty and abundancy.…”

Section: C Data Annotation Via Active Learningmentioning

confidence: 99%