Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
[HTML][HTML] Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
[HTML][HTML] Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies.
However, results have been limited to individual studies, lacking validation in multinational …
However, results have been limited to individual studies, lacking validation in multinational …
Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
[HTML][HTML] ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model
H Huang, O Zheng, D Wang, J Yin, Z Wang… - International Journal of …, 2023 - nature.com
The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-
4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with …
4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with …
[HTML][HTML] Deep learning in cancer pathology: a new generation of clinical biomarkers
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …
However, the growing number of these complex biomarkers tends to increase the cost and …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
The 2022 World Health Organization classification of tumors of the urinary system and male genital organs—part B: prostate and urinary tract tumors
Abstract The 2022 World Health Organization (WHO) classification of the urinary and male
genital tumors was recently published by the International Agency for Research on Cancer …
genital tumors was recently published by the International Agency for Research on Cancer …
Data-efficient and weakly supervised computational pathology on whole-slide images
Deep-learning methods for computational pathology require either manual annotation of
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection
Abstract Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019
and has spread rapidly all over the world since the beginning of 2020, has infected millions …
and has spread rapidly all over the world since the beginning of 2020, has infected millions …