Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis

Luo, Xiang-Ling; Qu, Linhao; Guo, Qinhao; Zhang, Song; Wang, Manning

doi:10.1109/jbhi.2023.3298798

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…The development of deep learning (DL) technology has had a profound impact in the field of medical image analysis [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Through its powerful data processing and feature recognition capabilities, deep learning can automatically extract and learn valuable information from complex MRI data, recognizing subtle patterns and variations that might be overlooked by human experts, providing new opportunities for the automated analysis of complex MRI data [31,32].…”

Section: Introductionmentioning

confidence: 99%

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network

Du,

Wang,

et al. 2024

Bioengineering

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In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

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