Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions
S Ali - npj Digital Medicine, 2022 - nature.com
Recent developments in deep learning have enabled data-driven algorithms that can reach
human-level performance and beyond. The development and deployment of medical image …
human-level performance and beyond. The development and deployment of medical image …
Polyp-pvt: Polyp segmentation with pyramid vision transformers
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …
when exchanging information between the encoder and decoder: 1) taking into account the …
Doubleu-net: A deep convolutional neural network for medical image segmentation
Semantic image segmentation is the process of labeling each pixel of an image with its
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …
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 …
Colonformer: An efficient transformer based method for colon polyp segmentation
Identifying polyps is challenging for automatic analysis of endoscopic images in computer-
aided clinical support systems. Models based on convolutional networks (CNN) …
aided clinical support systems. Models based on convolutional networks (CNN) …
A robust real-time deep learning based automatic polyp detection system
I Pacal, D Karaboga - Computers in Biology and Medicine, 2021 - Elsevier
Colorectal cancer (CRC) is globally the third most common type of cancer. Colonoscopy is
considered the gold standard in colorectal cancer screening and allows for the removal of …
considered the gold standard in colorectal cancer screening and allows for the removal of …
PolypSegNet: A modified encoder-decoder architecture for automated polyp segmentation from colonoscopy images
Colorectal cancer has become one of the major causes of death throughout the world. Early
detection of Polyp, an early symptom of colorectal cancer, can increase the survival rate to …
detection of Polyp, an early symptom of colorectal cancer, can increase the survival rate to …
Ensemble of instance segmentation models for polyp segmentation in colonoscopy images
Colorectal cancer is the second most frequently diagnosed cancer in women and the third
most frequently diagnosed cancer in men. At least 80%-95% of the colorectal cancers are …
most frequently diagnosed cancer in men. At least 80%-95% of the colorectal cancers are …
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in
their size, appearance, and location makes the detection of polyps challenging. Moreover …
their size, appearance, and location makes the detection of polyps challenging. Moreover …
Learn to threshold: Thresholdnet with confidence-guided manifold mixup for polyp segmentation
The automatic segmentation of polyp in endoscopy images is crucial for early diagnosis and
cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation …
cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation …