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Found 20 papers, 10 papers with code
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
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
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
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.
PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset curated by a team of computational scientists and expert gastroenterologists.
Few-shot segmentation of medical images based on meta-learning with implicit gradients
Classical supervised methods commonly used often suffer from the requirement of an abudant number of training samples and are unable to generalize on unseen datasets.
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation.
Ranked #1 on
Medical Image Segmentation
on Kvasir-SEG
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
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
Colorectal Polyps Characterization
Instrument Recognition
+3
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
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
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks.
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
Colorectal cancer is the third most common cause of cancer worldwide.
Automatic Polyp Segmentation using U-Net-ResNet50
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide.
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Ranked #8 on
Medical Image Segmentation
on CVC-ClinicDB
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
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
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
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
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
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.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
ResUNet++: An Advanced Architecture for Medical Image Segmentation
Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer.
Colorectal Polyps Characterization
Medical Image Segmentation
+1
Kvasir-SEG: A Segmented Polyp Dataset
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)
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract.