Weakly and Semi-supervised Deep Level Set Network for Automated Skin Lesion Segmentation

Deng, Zhuofu; Xin, Yi; Qiu, Xiaolin; Chen, Yeda

doi:10.1007/978-981-15-5852-8_14

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…FCOS advances its detection prowess by extracting features and blending them via both up and down sampling, applying pixel-level analysis across different scales to adeptly detect objects of various sizes. FCOS sets itself apart from other networks with its unique 'center-ness' feature, which leads to a comprehensive, anchor-free and proposal-free detection solution [22] [23].…”

Section: Research On Yolov3 and Fcos Object Detectionmentioning

confidence: 99%

Innovative Deep Learning Methods for Precancerous Lesion Detection

Gong,

Zhang,

et al. 2024

IJIRCST

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With the continuous advancement of socio-economic levels and relentless innovation in modern medical technologies, there's been a significant increase in the importance people place on their physiological health, particularly in the context of colorectal cancer—a prevalent malignant tumor that has captivated widespread attention within the medical community for its prevention and treatment. Notably, colorectal polyps, identified as precursors to colorectal cancer, are crucial for early diagnosis and precise detection, serving as fundamental elements in averting the disease and diminishing both its incidence and mortality rates. The swift progression of deep neural network technology in recent years has revolutionized computer-assisted medical diagnosis, especially for the detection of colorectal polyps. Deep learning technology, with its robust capability for feature learning and representation, has emerged as an invaluable aid for physicians, markedly enhancing diagnostic accuracy and efficiency. This study centers on colorectal polyps, striving to develop a detection model with superior accuracy by meticulously analyzing contemporary leading target detection algorithms. By fully exploiting the potent capabilities of deep neural networks, the model aims to boost the precision of colorectal polyp detection significantly, aiding physicians in elevating detection efficiency and simplifying diagnostic processes. By undertaking this research, we aim to make a significant contribution toward more accurate and efficient technological support for the early diagnosis and prevention of colorectal polyps, thereby aiding in the reduction of both the incidence and mortality rates associated with colorectal cancer.

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Section: Research On Yolov3 and Fcos Object Detectionmentioning

confidence: 99%

Innovative Deep Learning Methods for Precancerous Lesion Detection

Gong,

Zhang,

et al. 2024

IJIRCST

View full text Add to dashboard Cite

show abstract

“…which encourages ŷ1 il to be greater than ŷ0 i j plus margin. Similar to rank loss, narrowband suppression loss (Deng et al, 2020) also adds a constraint between hard-topredict pixels of background and lesion. Different from rank loss, narrowband suppression loss collects pixels in a narrowband along the ground-truth lesion boundary with radius r instead of all image pixels and then selects the top K pixels with the largest prediction errors.…”

Section: Rank Lossmentioning

confidence: 99%

A Survey on Deep Learning for Skin Lesion Segmentation

Mirikharaji¹,

Barata²,

Kumar³

et al. 2022

Preprint

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Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 134 research papers that deal with deep learning based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. We summarize all examined works in a comprehensive table to facilitate comparisons.

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“…Traditional methods for cancer cell detection often rely on subjective judgment and experience of physicians, leading to limitations such as time-consuming manual operations and susceptibility to subjective bias. However, with the advancement of deep learning technology, particularly in the field of object detection [1][2][3] [4], automated cancer cell detection methods based on deep learning are becoming an increasingly prominent trend [5] [6]. However, conventional object detectors cannot be applied directly to cancer cell detection due to various challenges, one of which arises from the limited inter-class variation observed between the malignant and the benign cancer cells [7].…”

Section: Introductionmentioning

confidence: 99%

Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System

Chen,

Hu,

et al. 2024

IJIRCST

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As artificial intelligence (AI) theory becomes more sophisticated and its utilization spreads across daily life, education, and professional settings, the adoption of AI for medical diagnostic and service purposes stands as a logical progression in the evolution of medical technologies. This document outlines a novel approach to detecting cancer cell targets using a deep learning-based system, marking a critical step towards integrating AI into cancer diagnostics. The process of detecting cancer cell targets entails the localization of cell types within images of cells. By capitalizing on the strengths of the YOLOv5 model—a deep learning-driven, end-to-end, real-time object detection framework known for its efficiency, superior performance, adaptability, and user-friendly PyTorch integration—this research presents an enhanced YOLOv5 model incorporating both a feature pyramid network and the original YOLOv5 architecture. The ultimate aim is to facilitate precise detection of targets in cancer cell images. The experimental data demonstrate the system's negligible error rate in detection, swift processing capabilities, and exceptional reliability.

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Weakly and Semi-supervised Deep Level Set Network for Automated Skin Lesion Segmentation

Cited by 6 publications

References 18 publications

Innovative Deep Learning Methods for Precancerous Lesion Detection

Innovative Deep Learning Methods for Precancerous Lesion Detection

A Survey on Deep Learning for Skin Lesion Segmentation

Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System

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