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相关论文: Automated Prostate Cancer Diagnosis Based on Gleas…

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Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason…

图像与视频处理 · 电气工程与系统科学 2022-12-27 Mohammad Mahdi Behzadi , Mohammad Madani , Hanzhang Wang , Jun Bai , Ankit Bhardwaj , Anna Tarakanova , Harold Yamase , Ga Hie Nam , Sheida Nabavi

Histopathological assessments, including surgical resection and core needle biopsy, are the standard procedures in the diagnosis of the prostate cancer. Current interpretation of the histopathology images includes the determination of the…

计算机视觉与模式识别 · 计算机科学 2017-05-12 Naiyun Zhou , Andrey Fedorov , Fiona Fennessy , Ron Kikinis , Yi Gao

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor…

Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to…

计算机视觉与模式识别 · 计算机科学 2020-05-26 Amartya Kalapahar , Julio Silva-Rodríguez , Adrián Colomer , Fernando López-Mir , Valery Naranjo

Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to…

计算机视觉与模式识别 · 计算机科学 2021-03-05 Santiago Toledo-Cortés , Diego H. Useche , Fabio A. González

Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in…

图像与视频处理 · 电气工程与系统科学 2021-05-24 Julio Silva-Rodríguez , Adrián Colomer , Jose Dolz , Valery Naranjo

The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was…

Advances in digital pathology and artificial intelligence (AI) offer promising opportunities for clinical decision support and enhancing diagnostic workflows. Previous studies already demonstrated AI's potential for automated Gleason…

Automated grading of prostate cancer histopathology images is a challenging task, with one key challenge being the scarcity of annotations down to the level of regions of interest (strong labels), as typically the prostate cancer Gleason…

计算机视觉与模式识别 · 计算机科学 2018-11-20 Eirini Arvaniti , Manfred Claassen

Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis. Although deep learning (DL) based segmentation methods achieve state-of-the-art accuracy, they rely on large datasets…

图像与视频处理 · 电气工程与系统科学 2021-10-04 Dwarikanath Mahapatra

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason…

Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two approaches for segmenting glandular structures in prostate images to automate Gleason grading.…

图像与视频处理 · 电气工程与系统科学 2025-01-23 Feda Bolus Al Baqain , Omar Sultan Al-Kadi

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between…

图像与视频处理 · 电气工程与系统科学 2023-03-22 Zahra Tabatabaei , Adrian colomer , Kjersti Engan , Javier Oliver , Valery Naranjo

The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of…

Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical…

图像与视频处理 · 电气工程与系统科学 2021-12-30 Ping-Chang Lin , Teodora Szasz , Hakizumwami B. Runesha

Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially…

图像与视频处理 · 电气工程与系统科学 2020-10-23 Hans Pinckaers , Wouter Bulten , Jeroen van der Laak , Geert Litjens

Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a huge…

Histology-based grade classification is clinically important for many cancer types in stratifying patients distinct treatment groups. In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate…

计算机视觉与模式识别 · 计算机科学 2019-11-07 Jingwen Wang , Richard J. Chen , Ming Y. Lu , Alexander Baras , Faisal Mahmood

Prostate cancer (PCa) is one of the most common cancers in men around the world. The most accurate method to evaluate lesion levels of PCa is microscopic inspection of stained biopsy tissue and estimate the Gleason score of tissue…

图像与视频处理 · 电气工程与系统科学 2020-05-12 Yi-hong Zhang , Jing Zhang , Yang Song , Chaomin Shen , Guang Yang

Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. The cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of…

图像与视频处理 · 电气工程与系统科学 2022-09-28 Alessandro Ferrero , Beatrice Knudsen , Deepika Sirohi , Ross Whitaker
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