Search Results for author:
Found 26 papers, 15 papers with code
Embryo 2.0: Merging Synthetic and Real Data for Advanced AI Predictions
Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 95% when trained solely on real data.
Comparative Analysis of Audio Feature Extraction for Real-Time Talking Portrait Synthesis
This paper examines the integration of real-time talking-head generation for interviewer training, focusing on overcoming challenges in Audio Feature Extraction (AFE), which often introduces latency and limits responsiveness in real-time applications.
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics.
SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset
The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics.
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
A \gls{lstm} trained on the soft cross-entropy outperformed conventional sleep detection algorithms, other neural network architectures and loss functions in accuracy and model calibration.
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
Mask-conditioned latent diffusion for generating gastrointestinal polyp images
Therefore, this study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given generated segmentation masks.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
VISEM-Tracking, a 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.
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.
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.
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.
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.
MMSys'22 Grand Challenge on AI-based Video Production for Soccer
This challenge aims to assist the automation of such a production pipeline using AI.
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.
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.
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.
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
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.
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology-based on video recordings of human spermatozoa.
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks.
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.
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.
