From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification

Zhang, Yifan; Chen, Hanbo; Wei, Ying; Zhao, Peilin; Cao, Jiezhang; Fan, Xinjuan; Lou, Xiaoying; Liu, Hailing; Hou, Jinlong; Han, Xiao; Yao, Jianhua; Wu, Qingyao; Tan, Mingkui; Huang, Junzhou

doi:10.1007/978-3-030-32239-7_40

Cited by 51 publications

(33 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach is based on unsupervised domain adaption, which aims to transfer knowledge from a labeled source domain to an unlabeled target domain [22]. The authors evaluate their method by benchmarking it on a medical image diagnosis dataset consisting of H&E stained colon histopathology slides [23] with a convincing performance.…”

Section: Noisy Labelsmentioning

confidence: 99%

A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications

Simmler

Sager²,

Andermatt³

et al. 2021

2021 8th Swiss Conference on Data Science (SDS)

View full text Add to dashboard Cite

When applying deep learning methods in an industrial vision application, they often fall short of the performance shown in a clean and controlled lab environment due to data quality issues. Few would consider the actual labels as a driving factor, yet inaccurate label data can impair model performance significantly. However, being able to mitigate inaccurate or incomplete labels might also be a cost-saver for real-world projects.Here, we survey state-of-the-art deep learning approaches to resolve such missing labels, noisy labels, and partially labeled data in the prospect of an industrial vision application. We systematically present un-, weakly, and semi-supervised approaches from 'A' like anomaly detection to 'Z' like zero-shot classification to resolve these challenges by embracing them.

show abstract

Section: Noisy Labelsmentioning

confidence: 99%

A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications

Simmler

Sager²,

Andermatt³

et al. 2021

2021 8th Swiss Conference on Data Science (SDS)

View full text Add to dashboard Cite

show abstract

“…For medical imaging, feature-based adversarial domain adaptation has been widely utilized for various applications. For example, in cross-modality adaptation, Zhang et al, [75] applied a domain discriminator to adapt models trained for pathology images to microscopy images. LSFT-DA is also used for single-modality adaptation to overcome dataset variations in pathology images, MR images, and ultrasound images.…”

Section: Adversarial Trainingmentioning

confidence: 99%

“…Thus, they focus on globally aligning domain distributions. However, the ability to transfer (or align) varies across clinical samples because of: (a) intra-domain variations (e.g., in multi-modal DA between MRI and CT, each modality can have contrast variations) [75]; (b) noisy annotations due to human subjectivity; (c) target label space being a subset of source label space [84]; and (d) varying transferability among different image regions [55] (e.g., tumors are difficult to translate and could be missed during CT to MRI image-translation [54]). Some samples in the source domain may be less useful and can lead to negative transferring [84], which adversely impacts DA.…”

Section: Transferability Of Individual Samplesmentioning

confidence: 99%

Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation

Choudhary

Zhu

et al. 2020

Yearb Med Inform

View full text Add to dashboard Cite

Introduction: There has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols. Domain adaptation (DA) has been developed to transfer the knowledge from a labeled data domain to a related but unlabeled domain in either image space or feature space. DA is a type of transfer learning (TL) that can improve the performance of models when applied to multiple different datasets. Objective: In this survey, we review the state-of-the-art DL-based DA methods for medical imaging. We aim to summarize recent advances, highlighting the motivation, challenges, and opportunities, and to discuss promising directions for future work in DA for medical imaging. Methods: We surveyed peer-reviewed publications from leading biomedical journals and conferences between 2017-2020, that reported the use of DA in medical imaging applications, grouping them by methodology, image modality, and learning scenarios. Results: We mainly focused on pathology and radiology as application areas. Among various DA approaches, we discussed domain transformation (DT) and latent feature-space transformation (LFST). We highlighted the role of unsupervised DA in image segmentation and described opportunities for future development. Conclusion: DA has emerged as a promising solution to deal with the lack of annotated training data. Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks. Opportunities include domain transferability, multi-modal DA, and applications that benefit from synthetic data.

show abstract

“…Recent work of Kamnitsas et al ( 2017 ) utilises an adversarial training approach for unsupervised domain adaptation from Gradient Echo images to Susceptibility Weighted Images for brain lesion segmentation task. Moreover, an adversarial domain adaptation from Whole Slide pathology to Microscopy images has been studied in Zhang et al ( 2019 ). Chen et al ( 2020 ) proposed simultaneous image to image translation and domain alignment between CT and MRI images using a modification of a CycleGAN for cardiac and abdominal multi-organ segmentation.…”

Section: Introductionmentioning

confidence: 99%

Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

et al. 2021

View full text Add to dashboard Cite

Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.

show abstract

From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification

Cited by 51 publications

References 12 publications

A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications

A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications

Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation

Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

Contact Info

Product

Resources

About