Segment anything model for medical images?
Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …
segmentation. It has achieved impressive results on various natural image segmentation …
A systematic review of deep learning based image segmentation to detect polyp
Among the world's most common cancers, colorectal cancer is the third most severe form of
cancer. Early polyp detection reduces the risk of colorectal cancer, vital for effective …
cancer. Early polyp detection reduces the risk of colorectal cancer, vital for effective …
Opportunities and challenges of synthetic data generation in oncology
Widespread interest in artificial intelligence (AI) in health care has focused mainly on
deductive systems that analyze available real-world data to discover patterns not otherwise …
deductive systems that analyze available real-world data to discover patterns not otherwise …
[HTML][HTML] ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification
JR Kaczmarzyk, R Gupta, TM Kurc… - Computer methods and …, 2023 - Elsevier
Abstract Background and Objective: Histopathology is the gold standard for diagnosis of
many cancers. Recent advances in computer vision, specifically deep learning, have …
many cancers. Recent advances in computer vision, specifically deep learning, have …
Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography
Background The epiretinal membrane (ERM) is a common retinal disorder characterized by
abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are …
abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are …
Application of histopathology image analysis using deep learning networks
As the rise in cancer cases, there is an increasing demand to develop accurate and rapid
diagnostic tools for early intervention. Pathologists are looking to augment manual analysis …
diagnostic tools for early intervention. Pathologists are looking to augment manual analysis …
A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy
GE Chung, J Lee, SH Lim, HY Kang, J Kim… - npj Digital …, 2024 - nature.com
This study evaluated the impact of differing false positive (FP) rates in two computer-aided
detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted …
detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted …
Impact of user's background knowledge and polyp characteristics in colonoscopy with computer-aided detection
J Lee, WS Cho, BS Kim, D Yoon, J Kim, JH Song… - Gut and …, 2024 - pmc.ncbi.nlm.nih.gov
Background/Aims We investigated how interactions between humans and computer-aided
detection (CADe) systems are influenced by the user's experience and polyp characteristics …
detection (CADe) systems are influenced by the user's experience and polyp characteristics …
From data to diagnosis: skin cancer image datasets for artificial intelligence
Artificial Intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum,
edging closer towards broad clinical use. These AI models, particularly deep learning …
edging closer towards broad clinical use. These AI models, particularly deep learning …
Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning
Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic
diseases and generating accurate reports. While recent studies have endeavored to identify …
diseases and generating accurate reports. While recent studies have endeavored to identify …