To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation.
Ranked #1 on Medical Image Segmentation on CVC-VideoClinicDB
Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery.
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation.
Ranked #1 on Medical Image Segmentation on Kvasir-SEG
To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.
Ranked #1 on Medical Image Segmentation on KvasirCapsule-SEG
With the increase in available large clinical and experimental datasets, there has been substantial amount of work being done on addressing the challenges in the area of biomedical image analysis.
Ranked #1 on Medical Image Segmentation on EM
Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks.
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Ranked #9 on Medical Image Segmentation on CVC-ClinicDB
1 code implementation • 23 Oct 2020 • Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven A. Hicks, VajiraThambawita, Enrique Garcia-Ceja, Michael A. Riegler, Thomas de Lange, Peter T. Schmidt, Håvard D. Johansen, Dag Johansen, Pål Halvorsen
Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development.
Ranked #2 on Medical Image Segmentation on Kvasir-Instrument
The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Ranked #1 on Medical Image Segmentation on Kvasir-Instrument
A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level.
In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.
Ranked #1 on Polyp Segmentation on Kvasir-SEG (DSC metric)
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract.