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Found 38 papers, 20 papers with code
RUPNet: Residual upsampling network for real-time polyp segmentation
Here, we propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation that can process in real-time and show high recall and precision.
Domain Generalization with Correlated Style Uncertainty
Hence, increasing the source domain diversity is a key component of domain generalization.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning.
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation
DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps.
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden.
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics
Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation
With high efficacy in our performance metrics, we concluded that TransResU-Net could be a strong benchmark for building a real-time polyp detection system for the early diagnosis, treatment, and prevention of colorectal cancer.
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network
We compare our FocalConvNet with other SOTA on Kvasir-Capsule, a large-scale VCE dataset with 44, 228 frames with 13 classes of different anomalies.
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide.
Transformer based Generative Adversarial Network for Liver Segmentation
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
TGANet: Text-guided attention for improved polyp segmentation
Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps.
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.
PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales.
GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation
The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network.
Ranked #6 on
Medical Image Segmentation
on Kvasir-SEG
MedAI: Transparency in Medical Image Segmentation
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
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-ColonDB
(using extra training data)
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.
Ranked #1 on
Medical Image Segmentation
on ROBUST-MIS
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.
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion 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.
Ranked #5 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets.
Ranked #3 on
Medical Image Segmentation
on 2018 Data Science Bowl
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
We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch.
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.
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.
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
Colorectal cancer is the third most common cause of cancer worldwide.
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
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.
Colorectal Polyps Characterization
Medical Image Segmentation
+3
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.
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.
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.
