Search Results for author:
Found 37 papers, 20 papers with code
VISEM-Tracking: Human Spermatozoa Tracking Dataset
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view.
MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning
We proposed two approaches; one using only the CT scans to make predictions and another using a combination of the CT and PET scans.
Combining datasets to increase the number of samples and improve model fitting
For many use cases, combining information from different datasets can be of interest to improve a machine learning model's performance, especially when the number of samples from at least one of the datasets is small.
Towards the Neuroevolution of Low-level Artificial General Intelligence
We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence (NAGI), a framework for low-level AGI.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem.
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift.
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer.
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging, where a change of hospital or in imaging routines can lead to a complete failure of a model.
Predicting tacrolimus exposure in kidney transplanted patients using machine learning
Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation.
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.
Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021
The Visual Sentiment Analysis task is being offered for the first time at MediaEval.
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.
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.
Artificial Intelligence in Dry Eye Disease
Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes.
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
DivergentNets: Medical Image Segmentation by Network Ensemble
For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches.
SinGAN-Seg: Synthetic training data generation for medical image segmentation
The pipeline is evaluated using qualitative and quantitative comparisons between real and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited.
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.
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.
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.
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Segmentation of findings in the gastrointestinal tract is a challenging but also an important task which is an important building stone for sufficient automatic decision support systems.
Real-Time Detection of Events in Soccer Videosusing 3D Convolutional Neural Networks
The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted.
Ranked #7 on
Action Spotting
on SoccerNet
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.
Efficient Quantile Tracking Using an Oracle
The MSE is decomposed in tracking variance and bias and novel and efficient procedures to estimate these quantities are presented.
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)
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
In the field of male human reproduction, clinical and biological data are not used to its fullest potential.
