Expression site agnostic histopathology image segmentation framework by self supervised domain adaption

He, Qiming; He, Ling; Duan, Hufei; Sun, Qiehe; Zheng, Runliang; Guan, Jian; He, Yonghong; Huang, Wenting; Guan, Tian

doi:10.1016/j.compbiomed.2022.106412

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…Self‐supervised learning is a form of machine learning where intrinsic feature distribution of the data is learned with the help of pretext tasks. It has been applied to medical image analysis and made significant improvements 33–35 . Tang et al 36 adopted self‐supervised learning to train vision transformers for multiple tasks of medical image analysis.…”

Section: Related Workmentioning

confidence: 99%

“…It has been applied to medical image analysis and made significant improvements. [33][34][35] Tang et al 36 adopted self-supervised learning to train vision transformers for multiple tasks of medical image analysis. Azizi et al 37 explored the effectiveness of selfsupervised pretraining based on ImageNet 38 and unlabeled medical image datasets.…”

Section: Self-supervised Learningmentioning

confidence: 99%

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Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

He,

Wang,

Zeng

et al. 2023

Int J Imaging Syst Tech

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Histopathological diagnosis is the golden standard for cancer diagnosis and grading. The past decade has witnessed great successes of vision‐based deep learning in computer‐aided cancer diagnosis. However, fully‐supervised methods require collecting large amounts of annotated data, which is a major challenge. In this research, we propose RMIL, a novel registration‐enhanced multiple instance learning pipeline, for cervical cancer biopsy grading of whole slide images as well as patch‐level regions. It requires only slide‐level annotations and a small amount of patch‐level annotations for training. To enhance it, an efficient registration method is implemented to generate more labeled patches with discriminative local features as data augmentations. In addition, attention mechanisms and self‐supervised learning are introduced to enhance feature extraction. Experiments showed that its classification accuracy is increased by 11.11%, compared to the vanilla multiple instance learning methods. The method has great potential for transferring to other scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Self-supervised Learningmentioning

confidence: 99%

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

He,

Wang,

Zeng

et al. 2023

Int J Imaging Syst Tech

Self Cite

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show abstract

“…Deep learning has demonstrated great application value in several medical fields. [9][10][11] Recently, it has achieved comparable performance in pathological image analysis, including classification, 12 segmentation, 13 detection, 14 and assisted diagnosis. [15][16][17] Furthermore, deep learning is increasingly playing on pathological advanced tasks, including gene prediction, 18,19 survival analysis, 20,21 virtual staining, 22 etc.…”

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

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The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.

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Generative Adversarial Networks for Self-Supervised Transfer Learning in Medical Image Classification

Kumar,

Yogeetha,

Savita

et al. 2024

Lecture Notes in Electrical Engineering

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Expression site agnostic histopathology image segmentation framework by self supervised domain adaption

Cited by 6 publications

References 45 publications

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Generative Adversarial Networks for Self-Supervised Transfer Learning in Medical Image Classification

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