126 citations · 308 across the 24 of their papers we have counts for
24 papers
Calliope: A TTS-based Narrated E-book Creator Ensuring Exact Synchronization, Privacy, and Layout Fidelity
Hugo L. Hammer, Vajira Thambawita, Pål Halvorsen
A narrated e-book combines synchronized audio with digital text, highlighting the currently spoken word or sentence during playback. This format supports early literacy and assists…
Anatomy-Preserving Latent Diffusion for Generation of Brain Segmentation Masks with Ischemic Infarct
Lucia Borrego, Vajira Thambawita, Marco Ciuffreda +3
The scarcity of high-quality segmentation masks remains a major bottleneck for medical image analysis, particularly in non-contrast CT (NCCT) neuroimaging, where manual annotation…
ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation
Vajira Thambawita, Jonas L. Isaksen, Jørgen K. Kanters +2
Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders their clinical deployment. Tr…
Medico 2025: Visual Question Answering for Gastrointestinal Imaging
Sushant Gautam, Vajira Thambawita, Michael Riegler +2
The Medico 2025 challenge addresses Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, organized as part of the MediaEval task series. The challenge focuses on deve…
Multimodal Integration Challenges in Emotionally Expressive Child Avatars for Training Applications
Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita +2
Dynamic facial emotion is essential for believable AI-generated avatars, yet most systems remain visually static, limiting their use in simulations like virtual training for invest…
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
Matthias Boeker, Vajira Thambawita, Michael Riegler +2
Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised lea…
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Debesh Jha, Vanshali Sharma, Debapriya Banik +30
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps durin…
Mask-conditioned latent diffusion for generating gastrointestinal polyp images
Roman Macháček, Leila Mozaffari, Zahra Sepasdar +4
In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in…
VISEM-Tracking, a human spermatozoa tracking dataset
Vajira Thambawita, Steven A. Hicks, Andrea M. Storås +7
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manu…
MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning
Vajira Thambawita, Andrea M. Storås, Steven A. Hicks +2
Head and neck cancers are the fifth most common cancer worldwide, and recently, analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed…
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Birk Torpmann-Hagen, Vajira Thambawita, Kyrre Glette +2
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…
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Jan Andre Fagereng, Vajira Thambawita, Andrea M. Storås +4
Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) sys…
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
Vladimir Monakhov, Vajira Thambawita, Pål Halvorsen +1
The interest for video anomaly detection systems has gained traction for the past few years. The current approaches use deep learning to perform anomaly detection in videos, but th…
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal +6
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial…
DivergentNets: Medical Image Segmentation by Network Ensemble
Vajira Thambawita, Steven A. Hicks, Pål Halvorsen +1
Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classifi…
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Vajira Thambawita, Steven Hicks, Pål Halvorsen +1
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 sy…
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Debesh Jha, Sharib Ali, Krister Emanuelsen +9
Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifica…
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
Vajira Thambawita, Debesh Jha, Hugo Lewi Hammer +4
Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identifica…
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2
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. First, we use an aut…
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2
In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and…
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Steven A. Hicks, Jorunn M. Andersen, Oliwia Witczak +5
Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human repr…
An optimized Parallel Failure-less Aho-Corasick algorithm for DNA sequence matching
Vajira Thambawita, Roshan G. Ragel, Dhammike Elkaduwe
The Aho-Corasick algorithm is multiple patterns searching algorithm running sequentially in various applications like network intrusion detection and bioinformatics for finding sev…
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
Vajira Thambawita, Debesh Jha, Michael Riegler +4
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neur…
To Use or Not to Use: CPUs' Cache Optimization Techniques on GPGPUs
Vajira Thambawita, Roshan G. Ragel, Dhammike Elkaduwe
General Purpose Graphic Processing Unit(GPGPU) is used widely for achieving high performance or high throughput in parallel programming. This capability of GPGPUs is very famous in…