2019
DOI: 10.3390/electronics8030256
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Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

Abstract: Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are posed by the high variability of the cancerous tissue c… Show more

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Cited by 26 publications
(27 citation statements)
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“…The success of this approach is mostly due to the possibility to use the large and versatile ImageNet [16] dataset as a source task [26]. Medical imaging and digital pathology communities have therefore studied and used transfer learning [9], [27]- [30] as it provides a way of coping with data scarcity. Those works have explored and evaluated different transfer 2 https://github.com/waliens/multitask-dipath techniques mostly using ImageNet as a source task.…”
Section: Related Workmentioning
confidence: 99%
“…The success of this approach is mostly due to the possibility to use the large and versatile ImageNet [16] dataset as a source task [26]. Medical imaging and digital pathology communities have therefore studied and used transfer learning [9], [27]- [30] as it provides a way of coping with data scarcity. Those works have explored and evaluated different transfer 2 https://github.com/waliens/multitask-dipath techniques mostly using ImageNet as a source task.…”
Section: Related Workmentioning
confidence: 99%
“…In other words, transfer the already acquired knowledge from different datasets to the WSIs context. This procedure appears in many state-of-the-art methods [43,44,45].…”
Section: Lack Of Annotated Samplesmentioning
confidence: 99%
“…Most of the available AI models have been trained on small data sets and can present a 20% drop of performance when applied in a setting different from where they had been originated. Dataset can be enlarged using specific technical solution such as the transfer learning approach [ 4 , 5 ]. Another possibility will be the development of open sources datasets such as those already hosted by the Cancer Genome Atlas, the Cancer Imaging Archives and Grand Challenges.…”
Section: Artificial Intelligence In Pathology: Future Perspetivesmentioning
confidence: 99%
“…As regard to the dimension of the dataset it is interesting to observe that convolutional neural network (CNN) can be trained on a source task and then be reused on a different target task. This technique, known as transfer learning, can be extremely useful when the data for the target task is scarce but a larger dataset is available to train the source task [ 4 , 5 ]. Finally, in unsupervised DL, the learning examples are provided with no associated labels.…”
Section: Introductionmentioning
confidence: 99%