Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
C McGenity, EL Clarke, C Jennings, G Matthews… - npj Digital …, 2024 - nature.com
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical
practice is essential. Growing numbers of studies using AI for digital pathology have been …
practice is essential. Growing numbers of studies using AI for digital pathology have been …
Empowering renal cancer management with AI and digital pathology: Pathology, diagnostics and prognosis
E Ivanova, A Fayzullin, V Grinin, D Ermilov… - Biomedicines, 2023 - mdpi.com
Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and
efficient diagnostic methods to guide treatment decisions. Traditional pathology practices …
efficient diagnostic methods to guide treatment decisions. Traditional pathology practices …
[HTML][HTML] A deep learning framework for automated classification of histopathological kidney whole-slide images
Background Renal cell carcinoma is the most common type of malignant kidney tumor and is
responsible for 14,830 deaths per year in the United States. Among the four most common …
responsible for 14,830 deaths per year in the United States. Among the four most common …
Artificial intelligence in pathomics and genomics of renal cell carcinoma
Broadly defined, artificial intelligence (AI) is the ability of a computer to model some form of
human interaction. AI as a concept can be traced as far back as third-century China with the …
human interaction. AI as a concept can be traced as far back as third-century China with the …
Feddbl: Communication and data efficient federated deep-broad learning for histopathological tissue classification
Histopathological tissue classification is a fundamental task in computational pathology.
Deep learning (DL)-based models have achieved superior performance but centralized …
Deep learning (DL)-based models have achieved superior performance but centralized …
Automatic classification of kidney CT images with relief based novel hybrid deep model
One of the most crucial organs in the human body is the kidney. Usually, the patient does not
realize the serious problems that arise in the kidneys in the early stages of the disease …
realize the serious problems that arise in the kidneys in the early stages of the disease …
Kidney Tumor Classification on CT images using Self-supervised Learning
One of the most common diseases affecting society around the world is kidney tumor. The
risk of kidney disease increases due to reasons such as consumption of ready-made food …
risk of kidney disease increases due to reasons such as consumption of ready-made food …
Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN
The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges
due to subjective interpretations, which affect the assessment of its severity. To address this …
due to subjective interpretations, which affect the assessment of its severity. To address this …
Context-aware self-supervised learning of whole slide images
M Aryal, NY Soltani - IEEE Transactions on Artificial Intelligence, 2024 - ieeexplore.ieee.org
Presenting whole slide images (WSIs) as graph will enable a more efficient and accurate
learning framework for cancer diagnosis. Due to the fact that a single WSI consists of billions …
learning framework for cancer diagnosis. Due to the fact that a single WSI consists of billions …
Classification of H&E Stained Liver Histopathology Images Using Ensemble Learning Techniques for Detection of the Level of Malignancy of Hepatocellular …
A Rukmangad, A Deshpande, A Jamthikar… - Advances in Artificial …, 2024 - Springer
Hepatocellular carcinoma (HCC) is one of the most common types of primary liver cancer
and a leading cause of cancer-related deaths worldwide. Diagnosis of the HCC using H&E …
and a leading cause of cancer-related deaths worldwide. Diagnosis of the HCC using H&E …