Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images

Xue, Tong-Chun; Chang, Heng; Ren, Min; Wang, Haochen; Yang, Yu; Wang, Boyang; Lv, Lei; Tang, Licheng; Fu, Chi-Cheng; Qu, Fang; He, Chu; Zhu, Xiaoli; Zhou, Xiaoyan; Bai, Qianming

doi:10.1038/s41598-023-36811-z

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…The purpose of this step is to learn, through training, how to map global information to another space in order to obtain the parameters needed for calculating attention weights. The sigmoid activation function is mainly useful in mapping the input to an output in the range (0, 1) as shown in Formula (10).…”

Section: Seam Attention Modulementioning

confidence: 99%

“…In recent years, the integration of medical imaging and computer science has become increasingly close. Medical image detection methods using deep learning have become a popular research topic [6][7][8][9][10][11]. However, medical image data are often large and complicated, and there are only limited labeled data available, which increases the difficulty of training deep learning models.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Jian,

Song,

Zhang

et al. 2024

Sensors

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Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.

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Section: Seam Attention Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Jian,

Song,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Another study demonstrated the potential of an AI-powered HER2 analyzer to mitigate interobserver variability and aid pathologists in achieving a consistent evaluation of HER2 expression levels [ 125 ]. Furthermore, several studies have used AI models to predict the amplification state of HER2 by analyzing digitized fluorescence ISH (FISH) images [ 126 127 128 ].…”

Section: Predictive or Prognostic Factors For Breast Cancermentioning

confidence: 99%

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Ahn,

Shin,

Yang

et al. 2023

J Breast Cancer

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Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the “black-box” conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

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A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management

Deng,

Yan,

Zhan

et al. 2024

iMetaOmics

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Breast cancer (BC), specifically HER2‐positives subtype, has a poor prognosis. Nevertheless, the development of anti‐HER2 therapy yielded satisfactory outcomes. Therefore, evaluating patient HER2 status and ascertaining responsiveness to anti‐HER2 therapy is crucial. The advent of deep learning has propelled the artificial intelligence (AI) revolution, leading to an increased applicability of AI in predictive models. In the field of medicine, AI is an emerging modality that is gaining momentum for facilitating cancer diagnosis and treatment, particularly in the effective management of breast cancer. This study aims to provide a comprehensive review of current diagnostic and predictive models that utilize data obtained from histopathological slides, radiomics, and HER2 binding sites. Advancements and practical applications of these models were also evaluated. Additionally, we examined existing obstacles that AI encounters for anti‐HER2 therapy. We also proposed future directions for integrating AI in assessing and managing anti‐HER2 therapy. The findings of this study offer valuable insights into the evaluation of AI‐based anti‐HER2 therapy, emphasizing key concepts and obstacles that, if addressed, could facilitate the integration of AI‐assisted anti‐HER2 therapy. The integration of AI has the potential to enhance the precision and customization of screening and treatment protocols for HER2+ breast cancer.

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Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images

Cited by 6 publications

References 36 publications

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management

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