Automated Ovarian Cancer Identification Using End-to-End Deep Learning and Second Harmonic Generation Imaging

Wang, Guangxing; Zhan, Huiling; Luo, Tianyi; Kang, Bingzi; Li, Xiaolu; Xi, Gangqin; Liu, Zhiyi; Zhuo, Shuangmu

doi:10.1109/jstqe.2022.3228567

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Saida et al [7] compared deep learning and radiologist assessments for MRI diagnosis but lacked the integration of advanced attention mechanisms. Wang et al [8] used end-to-end deep learning but did not employ attention-based models. Saba [9] conducted a survey of cancer detection using machine learning, highlighting the need for more advanced and accurate methods.…”

Section: Related Workmentioning

confidence: 99%

“…Conventionally, the diagnosis of ovarian cancer is grounded in medical imaging, including magnetic resonance imaging (MRI). Radiologists play a pivotal role in scrutinizing these images for signs of malignancy [7] [8]. While MRI offers superior soft tissue contrast, the interpretation is labor-intensive and is subject to inter-observer variability.…”

Section: Introductionmentioning

confidence: 99%

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ABM-OCD: Advancing ovarian cancer diagnosis with attention-based models and 3D CNNs

Jenefa,

Edward,

Ebenezer

et al. 2023

ITEGAM

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Ovarian cancer remains a leading cause of cancer-related mortality among women worldwide. Traditional diagnostic methods often lack the precision required for early detection and accurate subtype classification. In this study, we address the challenge of automating ovarian cancer diagnosis by introducing Attention-Based Models (ABMs) in combination with 3D Convolutional Neural Networks (CNNs). Our research seeks to enhance the accuracy and efficiency of ovarian cancer diagnosis, particularly in distinguishing between serous, mucinous, and endometrioid subtypes. Conventional diagnostic approaches are limited by their reliance on manual interpretation of medical images and fail to fully exploit the rich information present in MRI scans. The proposed work leverages ABMs to dynamically focus on critical regions in MRI scans, enabling enhanced feature extraction and improved classification accuracy. We demonstrate our approach on a well-curated dataset, OvaCancerMRI-2023, showcasing the potential for precise and automated diagnosis. Experimental results indicate superior performance in cancer subtype classification compared to traditional methods, with an accuracy of 94% and F1 score of 0.92. Our findings underscore the potential of ABMs and 3D CNNs in revolutionizing ovarian cancer diagnosis, paving the way for early intervention and more effective treatment strategies. In conclusion, this research marks a significant advancement in the realm of ovarian cancer diagnosis, offering a promising avenue for improving patient outcomes and reducing the burden of this devastating disease. The integration of ABMs and 3D CNNs holds substantial potential for enhancing the accuracy and efficiency of ovarian cancer diagnosis, particularly in subtyping, and may contribute to early intervention and improved patient care.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ABM-OCD: Advancing ovarian cancer diagnosis with attention-based models and 3D CNNs

Jenefa,

Edward,

Ebenezer

et al. 2023

ITEGAM

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show abstract

“…We provide ample examples of different studies focusing on different applications. In [12] a classification application, a method for diagnosing ovarian cancer during surgery using SHG imaging and deep learning techniques is introduced. By training a convolutional neural network (CNN) on a vast dataset of SHG images, the system can differentiate between normal, benign, and malignant ovarian tissues with 99.7% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of CARE 2D and N2V 2D for tissue‐specific denoising in second harmonic generation imaging

Aghigh,

Jargot,

Zaouter

et al. 2024

Journal of Biophotonics

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This study explored the application of deep learning in second harmonic generation (SHG) microscopy, a rapidly growing area. This study focuses on the impact of glycerol concentration on image noise in SHG microscopy and compares two image restoration techniques: Noise‐to‐Void 2D (N2V 2D, no reference image restoration) and content‐aware image restoration (CARE 2D, full reference image restoration). We demonstrated that N2V 2D effectively restored the images affected by high glycerol concentrations. To reduce sample exposure and damage, this study further addresses low‐power SHG imaging by reducing the laser power by 70% using deep learning techniques. CARE 2D excels in preserving detailed structures, whereas N2V 2D maintains natural muscle structure. This study highlights the strengths and limitations of these models in specific SHG microscopy applications, offering valuable insights and potential advancements in the field .

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DeepOvaNet: A Comprehensive Deep Learning Framework for Predicting and Diagnosing Ovarian Cancer in Women Across Menopausal Transitions

Das,

Chilakarao,

Biswas

et al. 2024

2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Automated Ovarian Cancer Identification Using End-to-End Deep Learning and Second Harmonic Generation Imaging

Cited by 5 publications

References 41 publications

ABM-OCD: Advancing ovarian cancer diagnosis with attention-based models and 3D CNNs

ABM-OCD: Advancing ovarian cancer diagnosis with attention-based models and 3D CNNs

A comparative study of CARE 2D and N2V 2D for tissue‐specific denoising in second harmonic generation imaging

DeepOvaNet: A Comprehensive Deep Learning Framework for Predicting and Diagnosing Ovarian Cancer in Women Across Menopausal Transitions

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