Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation

Hataya, Ryuichiro; Zdenek, Jan; Yoshizoe, Kazuki; Nakayama, Hideki

doi:10.1007/978-3-030-58595-2_1

Cited by 113 publications

(109 citation statements)

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“…This transfers the style of one image onto another image during training. Furthermore, two augmentations created with AutoAlbument [ 25 ] were investigated. This set of augmentations was picked to investigate whether complex augmentations help the networks to generalize better on the training data or prevent them from learning the relevant features.…”

Section: Methodsmentioning

confidence: 99%

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis

et al. 2021

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When approaching thyroid gland tumor classification, the differentiation between samples with and without “papillary thyroid carcinoma-like” nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen’s Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen’s Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen’s Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen’s Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.

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Section: Methodsmentioning

confidence: 99%

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis

et al. 2021

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“…Moreover, our results also show that selecting a well-parametrized augmentation sequence and learning rate schedules can be crucial to improve the performance. Automating the training process for instance by using tools like FasterAutoAugment [26] could lead to further performance boosts or reduced engineering times in the future.…”

Section: Discussionmentioning

confidence: 99%

On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned

Scherr

Loeffler

Neumann

et al. 2021

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The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task. Especially in low-resolution and low signal-to-noise-ratio microscopy images erroneously merged and missing cells are common segmentation errors making the subsequent cell tracking even more difficult. In 2020, we successfully participated as team KIT-Sch-GE (1) in the 5th edition of the ISBI Cell Tracking Challenge. With our deep learning-based distance map regression segmentation and our graph-based cell tracking, we achieved multiple top 3 rankings on the diverse data sets. In this manuscript, we show how our approach can be further improved by using another optimizer and by fine-tuning training data augmentation parameters, learning rate schedules, and the training data representation. The fine-tuned segmentation in combination with an improved tracking enabled to further improve our performance in the 6th edition of the Cell Tracking Challenge 2021 as team KIT-Sch-GE (2).

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“…It increases both the amount and diversity of data by randomly "augmenting" it [82]. AutoAugment is a way to automatically search for improved data augmentation policies [83], [84], where reinforcement learning is used to search for better data augmentation policies. Fig.…”

Section: Active Research Problems In Deep Learningmentioning

confidence: 99%

On Deep Research Problems in Deep Learning

Ulhaq¹

2021

Preprint

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The subject of deep learning has emerged in the last decade as one of the most promising approaches to machine learning. Today, certainly, much of the recent progress in artificial intelligence is due to it, but research challenges are still unresolved and remain open to the research community. This paper attempts to offer a comprehensive review of deep learning progress in active research frontiers. On the one side, by presenting a brief overview of deep learning success, we inspire researchers to work in deep learning. On the other hand, we examine a range of technical issues, and open research issues that we believe are relevant topics for exploratory research. As deep learning applies to various fields, we restrict this paper’s scope to visual recognition tasks to analyze these problems with a specific lens. However, these problems will be broadly applicable to other fields. It will make it easier for new researchers to recognize outstanding research problems in the deep learning domain.

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Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation

Cited by 113 publications

References 12 publications

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis

On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned

On Deep Research Problems in Deep Learning

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