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CRC-HGD: A Histopathological Image Dataset for Grading Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma. In recent years, artificial intelligence and its subcategories, including machine learning and deep learning, have been increasingly employed for automated cancer detection and classification. An appropriate and well-organized dataset is the essential first step to achieve thi

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newsExplainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study - NaturenewsMachine learning redefines precision cancer therapy using smart radiopharmaceuticals - News-MedicalnewsMachine learning-driven pathogen cluster analysis identifies high-risk subtypes of infected pancreatic necrosis in a multi-center cohort - Baishideng Publishing Group

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modelTongyi-MAI/Z-Image-Turbo

Covers (incoming)

newsGC–MS and machine learning identify urinary bladder cancer signature - Chromatography TodaynewsBreast cancer detection and classification via a robust deep learning approach - NaturenewsIntegrated data and machine learning transform lung cancer diagnosis and treatment - Bioengineer.orgnewsDeep Learning-Based Oral Cancer Detection Using Clinical Images - CureusnewsAI detection not automatically better for colorectal cancer screening in Lynch syndrome, study shows - Medical XpressnewsDual-phase deep learning models improve pancreatic cyst risk assessment - News-MedicalnewsCloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging - CureusnewsClassification of Benign Hematogones and B-cell Acute Lymphoblastic Leukemia in Peripheral Blood Smear Images Using Artificial Intelligence - CureusnewsMachine learning survival models for colorectal cancer: advantages of random survival forests over classification algorithms - Nature

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personElham AmjadipersonAmin BahreinipersonSayed Mohammad Hasan EmamipersonSayyed Mohammadreza HakimianpersonAlireza FahimpersonHojjatollah RahimipersonHamidreza Bolhasani

Related across the graph

newsMachine learning-driven pathogen cluster analysis identifies high-risk subtypes of infected pancreatic necrosis in a multi-center cohort - Baishideng Publishing GroupnewsDeep Learning-Based Oral Cancer Detection Using Clinical Images - CureusnewsMachine learning survival models for colorectal cancer: advantages of random survival forests over classification algorithms - NaturepersonSayed Mohammad Hasan EmamipersonSayyed Mohammadreza HakimiannewsDual-phase deep learning models improve pancreatic cyst risk assessment - News-MedicalnewsCloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging - CureusnewsMachine learning redefines precision cancer therapy using smart radiopharmaceuticals - News-MedicalnewsBreast cancer detection and classification via a robust deep learning approach - NaturepersonHojjatollah RahiminewsGC–MS and machine learning identify urinary bladder cancer signature - Chromatography TodaynewsExplainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study - NaturemodelTongyi-MAI/Z-Image-TurbopersonElham AmjadipersonAlireza FahimnewsAI detection not automatically better for colorectal cancer screening in Lynch syndrome, study shows - Medical XpresspersonHamidreza BolhasaninewsClassification of Benign Hematogones and B-cell Acute Lymphoblastic Leukemia in Peripheral Blood Smear Images Using Artificial Intelligence - CureuspersonAmin BahreininewsIntegrated data and machine learning transform lung cancer diagnosis and treatment - Bioengineer.org

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