Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for Histopathology Image Interpretable Classification

Yang, Mei; Xie, Zhiying; Wang, Zhaoxia; Yuan, Yun; Zhang, Jue

doi:10.1109/tmi.2022.3188326

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…The two-stage MIL 21 , 22 training pipeline demonstrated the feasibility of classifying digitalized histopathology of brain tumors. The general structure of the MIL was introduced in our previous study.…”

Section: Resultsmentioning

confidence: 99%

A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

Jin,

Sun,

Liu

et al. 2023

iScience

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Section: Resultsmentioning

confidence: 99%

A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

Jin,

Sun,

Liu

et al. 2023

iScience

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“…(2) Emulating the training routines followed by radiologists by gradually increasing task difficulty through methods like Curriculum Learning (CL): Gracias et al [26] applied the Selfpaced Curriculum Learning strategy to 3D-CNN as a method that combines medical knowledge to enhance the performance of early Alzheimer's disease diagnosis networks. Yang et al [27] proposed severity-guided multiple instance curriculum learning, a curriculum that progresses from easy to difficult, utilizing image training models for the classification of pathological images. (3) Using attention mechanisms and other methods to mimic the specific regions or sequences that doctors focus on.…”

Section: Related Workmentioning

confidence: 99%

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Xue,

Lu,

Wang

et al. 2024

PLoS ONE

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In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.

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EH-former: Regional easy-hard-aware transformer for breast lesion segmentation in ultrasound images

Qu,

Zhou,

Jiang

et al. 2024

Information Fusion

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Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for Histopathology Image Interpretable Classification

Cited by 4 publications

References 41 publications

A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

EH-former: Regional easy-hard-aware transformer for breast lesion segmentation in ultrasound images

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