A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area

Chen, Liuyin; Qi, Haoyang; Lu, Di; Zhai, Jianxue; Cai, Kaican; Wang, Long; Liang, Guoyuan

doi:10.1016/j.xpro.2022.101485

Cited by 4 publications

(1 citation statement)

References 5 publications

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“… 1 This protocol provides a detailed process for preparing the fundus image dataset, and model training, inference, and evaluation steps for DeepFundus. 7 , 8 The protocol setup described here specifically applies to the Ubuntu 18.04 LTS Linux distribution. Nevertheless, the referenced codes as well as packages are designed to operate appropriately on various computer platforms, such as Linux, Windows, and Mac OS.…”

Section: Before You Beginmentioning

confidence: 99%

Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques

Liu,

Li,

Lin

et al. 2023

STAR Protocols

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Section: Before You Beginmentioning

confidence: 99%

Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques

Liu,

Li,

Lin

et al. 2023

STAR Protocols

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What can machine vision do for lymphatic histopathology image analysis: a comprehensive review

Chen,

Li,

et al. 2024

Artif Intell Rev

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Over the past 10 years, machine vision (MV) algorithms for image analysis have been developing rapidly with computing power. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide diagnostic references to doctors. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the application of image processing techniques based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some potential methods are proposed, and further prospects are made.

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Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends

Aleem,

Khan,

Younes

et al. 2024

Current Oncology

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Artificial intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small-cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical record (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal the potential to improve surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and perioperative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

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A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area

Cited by 4 publications

References 5 publications

Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques

Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques

What can machine vision do for lymphatic histopathology image analysis: a comprehensive review

Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends

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