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 …
Real-time polyp detection, localization and segmentation in colonoscopy using deep learning
Computer-aided detection, localization, and segmentation methods can help improve
colonoscopy procedures. Even though many methods have been built to tackle automatic …
colonoscopy procedures. Even though many methods have been built to tackle automatic …
Cat-seg: Cost aggregation for open-vocabulary semantic segmentation
Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …
within an image based on a wide range of text descriptions. In this work we introduce a …
[HTML][HTML] Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Abstract The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative
to address eminent problems in developing reliable computer aided detection and diagnosis …
to address eminent problems in developing reliable computer aided detection and diagnosis …
Overview of the ImageCLEF 2024: multimedia retrieval in medical applications
This paper presents an overview of the ImageCLEF 2024 lab, organized as part of the
Conference and Labs of the Evaluation Forum–CLEF Labs 2024. ImageCLEF, an ongoing …
Conference and Labs of the Evaluation Forum–CLEF Labs 2024. ImageCLEF, an ongoing …
What a mess: Multi-domain evaluation of zero-shot semantic segmentation
While semantic segmentation has seen tremendous improvements in the past, there are still
significant labeling efforts necessary and the problem of limited generalization to classes …
significant labeling efforts necessary and the problem of limited generalization to classes …
Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art
T Rueckert, D Rueckert, C Palm - Computers in Biology and Medicine, 2024 - Elsevier
In the field of computer-and robot-assisted minimally invasive surgery, enormous progress
has been made in recent years based on the recognition of surgical instruments in …
has been made in recent years based on the recognition of surgical instruments in …
Endomapper dataset of complete calibrated endoscopy procedures
P Azagra, C Sostres, Á Ferrández, L Riazuelo… - Scientific Data, 2023 - nature.com
Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most
research focuses on the automatic detection of polyps or other pathologies, but localization …
research focuses on the automatic detection of polyps or other pathologies, but localization …
U-Net model with transfer learning model as a backbone for segmentation of gastrointestinal tract
The human gastrointestinal (GI) tract is an important part of the body. According to World
Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In …
Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In …
TMF-Net: A transformer-based multiscale fusion network for surgical instrument segmentation from endoscopic images
Automatic surgical instrument segmentation is a necessary step for the steady operation of
surgical robots, and the segmentation accuracy directly affects the surgical effect …
surgical robots, and the segmentation accuracy directly affects the surgical effect …