Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling

Lai, Zhengfeng; Wang, Chao; Oliveira, Luca Cerny; Dugger, Brittany N.; Cheung, Susan; Chuah, Chen-Nee

doi:10.1109/iccvw54120.2021.00072

Cited by 32 publications

(13 citation statements)

References 32 publications

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“…Although much progress has been achieved, the superior performance of previous deep neural network-based methods mainly depends on the substantial number of training images with pixel-wise annotation, which are difficult to obtain due to the requirements of tremendous labeling efforts for experts. In order to reduce the overall labelling cost, several weakly supervised tissue segmentation algorithms have also been proposed [ 53 , 95 , 96 ]. For instance, Mahapatra [ 95 ] proposed a deep active learning framework that could actively select valuable samples from the unlabeled data for annotation, which significantly reduced the annotation efforts while still achieving comparable gland segmentation performance.…”

Section: Pathology Image Segmentationmentioning

confidence: 99%

“…For instance, Mahapatra [ 95 ] proposed a deep active learning framework that could actively select valuable samples from the unlabeled data for annotation, which significantly reduced the annotation efforts while still achieving comparable gland segmentation performance. Lai et al [ 96 ] proposed a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for annotation queries to quickly expand the diversity and volume of the labeled set.…”

Section: Pathology Image Segmentationmentioning

confidence: 99%

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Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Cheng

Huang

et al. 2022

Cancers

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With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

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Section: Pathology Image Segmentationmentioning

confidence: 99%

Section: Pathology Image Segmentationmentioning

confidence: 99%

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Cheng

Huang

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…An active learning method with a core-set sampling strategy [118] tackled this challenge by merging uncertain patches into regions for annotation; this strategy did not affect the training procedure of the classifier that still operated on patches. To effectively leverage the information in labeled and unlabeled data, Lai et al [119] proposed a label-efficient framework with active learning and semi-supervised learning for brain tissue segmentation in gigapixel pathology images, which surpassed fully supervised learning methods by using only 0.1% annotations.…”

Section: Deep Learning With Small But Expensive Datasetmentioning

confidence: 99%

Deep Learning in Cell Image Analysis

Zhou

Deng

et al. 2022

Intell Comput

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Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, cell imaging is frequently performed in a high-content manner. Consequently, the manual interpretation of cell images becomes extremely inefficient. Fortunately, with the advancement of deep-learning technologies, an increasing number of deep learning-based algorithms have been developed to automate and streamline this process. In this study, we present an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification. Despite the impressive score, the challenge still remains: most of the algorithms only verify the performance in their customized settings, causing a performance gap between academic research and practical application. Thus, we also review more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.

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“…Some methods are even able to detect the cancer subtypes [33] or detect the spread of cancer to lymph nodes (metastasis) [36]. Semantic segmentation of such images can be useful in neuropathology [37], which is the study of diseases of the nervous system, and identifying tissue components such as tumor, muscle, and debris in medical images [38].…”

Section: A Medical and Biomedical Image Analysismentioning

confidence: 99%

Efficient High-Resolution Deep Learning: A Survey

Bakhtiarnia¹,

Zhang²,

Iosifidis³

2022

Preprint

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Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many other applications. Using high-resolution images and videos as direct inputs for deep learning models creates many challenges due to their high number of parameters, computation cost, inference latency and GPU memory consumption. Simple approaches such as resizing the images to a lower resolution are common in the literature, however, they typically significantly decrease accuracy. Several works in the literature propose better alternatives in order to deal with the challenges of high-resolution data and improve accuracy and speed while complying with hardware limitations and time restrictions. This survey describes such efficient high-resolution deep learning methods, summarizes realworld applications of high-resolution deep learning, and provides comprehensive information about available high-resolution datasets.

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Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling

Cited by 32 publications

References 32 publications

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Deep Learning in Cell Image Analysis

Efficient High-Resolution Deep Learning: A Survey

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