no code implementations • 31 Jan 2025 • Nhan Phan, Thu Nguyen, Pål Halvorsen, Michael A. Riegler
Therefore, it is also widely used on the data prior to training a neural network.
no code implementations • 17 Jan 2025 • Tuan L. Vo, Quan Huu Do, Uyen Dang, Thu Nguyen, Pål Halvorsen, Michael A. Riegler, Binh T. Nguyen
In this paper, we propose Direct Parameter Estimation for Randomly Missing Data with Categorical Features (DPERC), an efficient approach for direct parameter estimation tailored to mixed data that contains missing values within continuous features.
no code implementations • 17 Dec 2024 • Syed Zohaib Hassan, Pierre Lison, Pål Halvorsen
We demonstrate through a user study that the insertion of disfluencies significantly increase the perceived spontaneity of the generated speech.
no code implementations • 15 Dec 2024 • Lien P. Le, Xuan-Hien Nguyen Thi, Thu Nguyen, Michael A. Riegler, Pål Halvorsen, Binh T. Nguyen
Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions.
1 code implementation • 20 Nov 2024 • Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita, Sushant Gautam, Saeed S. Sabet, Dag Johansen, Michael A. Riegler, Pål Halvorsen
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.
no code implementations • 29 Oct 2024 • Finn Bartels, Lu Xing, Cise Midoglu, Matthias Boeker, Toralf Kirsten, Pål Halvorsen
We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML).
no code implementations • 26 Sep 2024 • Zahra Sepasdar, Sushant Gautam, Cise Midoglu, Michael A. Riegler, Pål Halvorsen
Extracting meaningful insights from large and complex datasets poses significant challenges, particularly in ensuring the accuracy and relevance of retrieved information.
1 code implementation • 2 Sep 2024 • Sushant Gautam, Andrea Storås, Cise Midoglu, Steven A. Hicks, Vajira Thambawita, Pål Halvorsen, Michael A. Riegler
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.
1 code implementation • 19 Aug 2024 • Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Quoc-Huy Trinh, Koushik Biswas, Hongyi Pan, Ritika K. Jha, Gorkem Durak, Alexander Hann, Jonas Varkey, Hang Viet Dao, Long Van Dao, Binh Phuc Nguyen, Nikolaos Papachrysos, Brandon Rieders, Peter Thelin Schmidt, Enrik Geissler, Tyler Berzin, Pål Halvorsen, Michael A. Riegler, Thomas de Lange, Ulas Bagci
More information about the dataset, segmentation, detection, federated learning benchmark and train-test split can be found at \url{https://github. com/DebeshJha/PolypDB}.
no code implementations • 22 Jul 2024 • Håkon Maric Solberg, Mehdi Houshmand Sarkhoosh, Sushant Gautam, Saeed Shafiee Sabet, Pål Halvorsen, Cise Midoglu
In the rapidly evolving field of sports analytics, the automation of targeted video processing is a pivotal advancement.
1 code implementation • 3 Jun 2024 • Aleksander Theo Strand, Sushant Gautam, Cise Midoglu, Pål Halvorsen
The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets.
1 code implementation • 3 Jun 2024 • Aleksander Theo Strand, Sushant Gautam, Cise Midoglu, Pål Halvorsen
The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets.
1 code implementation • 12 May 2024 • Sushant Gautam, Mehdi Houshmand Sarkhoosh, Jan Held, Cise Midoglu, Anthony Cioppa, Silvio Giancola, Vajira Thambawita, Michael A. Riegler, Pål Halvorsen, Mubarak Shah
The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics.
no code implementations • 27 Feb 2024 • Matthias Boeker, Vajira Thambawita, Michael Riegler, Pål Halvorsen, Hugo L. Hammer
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.
1 code implementation • 28 Nov 2023 • Thu Nguyen, Tuan L. Vo, Pål Halvorsen, Michael A. Riegler
We propose Classification Based on MissForest Imputation (CBMI), a classification strategy that initializes the predicted test label with missing values and stacks the label with the input for imputation, allowing the label and the input to be imputed simultaneously.
1 code implementation • 30 Jul 2023 • Debesh Jha, Vanshali Sharma, Debapriya Banik, Debayan Bhattacharya, Kaushiki Roy, Steven A. Hicks, Nikhil Kumar Tomar, Vajira Thambawita, Adrian Krenzer, Ge-Peng Ji, Sahadev Poudel, George Batchkala, Saruar Alam, Awadelrahman M. A. Ahmed, Quoc-Huy Trinh, Zeshan Khan, Tien-Phat Nguyen, Shruti Shrestha, Sabari Nathan, Jeonghwan Gwak, Ritika K. Jha, Zheyuan Zhang, Alexander Schlaefer, Debotosh Bhattacharjee, M. K. Bhuyan, Pradip K. Das, Deng-Ping Fan, Sravanthi Parsa, Sharib Ali, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Ulas Bagci
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
1 code implementation • 16 Jul 2023 • Debesh Jha, Vanshali Sharma, Neethi Dasu, Nikhil Kumar Tomar, Steven Hicks, M. K. Bhuyan, Pradip K. Das, Michael A. Riegler, Pål Halvorsen, Ulas Bagci, Thomas de Lange
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance.
no code implementations • 10 May 2023 • Tu T. Do, Mai Anh Vu, Tuan L. Vo, Hoang Thien Ly, Thu Nguyen, Steven A. Hicks, Michael A. Riegler, Pål Halvorsen, Binh T. Nguyen
To address this issue, we propose a Blockwise principal component analysis Imputation (BPI) framework for dimensionality reduction and imputation of monotone missing data.
no code implementations • 10 May 2023 • Nhat-Hao Pham, Khanh-Linh Vo, Mai Anh Vu, Thu Nguyen, Michael A. Riegler, Pål Halvorsen, Binh T. Nguyen
Correlation matrix visualization is essential for understanding the relationships between variables in a dataset, but missing data can pose a significant challenge in estimating correlation coefficients.
no code implementations • 11 Apr 2023 • Roman Macháček, Leila Mozaffari, Zahra Sepasdar, Sravanthi Parasa, Pål Halvorsen, Michael A. Riegler, Vajira Thambawita
Therefore, this study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given generated segmentation masks.
1 code implementation • 3 Apr 2023 • Duc-Tien Dang-Nguyen, Sohail Ahmed Khan, Cise Midoglu, Michael Riegler, Pål Halvorsen, Minh-Son Dao
OOC media is much harder to detect than fake media, since the images and videos are not tampered.
1 code implementation • 2 Feb 2023 • Mai Anh Vu, Thu Nguyen, Tu T. Do, Nhan Phan, Nitesh V. Chawla, Pål Halvorsen, Michael A. Riegler, Binh T. Nguyen
Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance.
1 code implementation • 6 Dec 2022 • Vajira Thambawita, Steven A. Hicks, Andrea M. Storås, Thu Nguyen, Jorunn M. Andersen, Oliwia Witczak, Trine B. Haugen, Hugo L. Hammer, Pål Halvorsen, Michael A. Riegler
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view.
1 code implementation • 30 Nov 2022 • Vajira Thambawita, Andrea M. Storås, Steven A. Hicks, Pål Halvorsen, Michael A. Riegler
We proposed two approaches; one using only the CT scans to make predictions and another using a combination of the CT and PET scans.
1 code implementation • 11 Oct 2022 • Thu Nguyen, Rabindra Khadka, Nhan Phan, Anis Yazidi, Pål Halvorsen, Michael A. Riegler
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.
1 code implementation • 27 Jul 2022 • Sidney Pontes-Filho, Kristoffer Olsen, Anis Yazidi, Michael A. Riegler, Pål Halvorsen, Stefano Nichele
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.
no code implementations • 30 May 2022 • Thu Nguyen, Hoang Thien Ly, Michael Alexander Riegler, Pål Halvorsen, Hugo L. Hammer
Missing data is a commonly occurring problem in practice.
1 code implementation • 30 May 2022 • Birk Torpmann-Hagen, Vajira Thambawita, Kyrre Glette, Pål Halvorsen, Michael A. Riegler
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.
1 code implementation • 30 May 2022 • Jan Andre Fagereng, Vajira Thambawita, Andrea M. Storås, Sravanthi Parasa, Thomas de Lange, Pål Halvorsen, Michael A. Riegler
Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer.
1 code implementation • 30 May 2022 • Vladimir Monakhov, Vajira Thambawita, Pål Halvorsen, Michael A. Riegler
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.
no code implementations • 9 May 2022 • Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler, Inga Strümke
Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation.
1 code implementation • 23 Mar 2022 • Steven Hicks, Andrea Storås, Michael Riegler, Cise Midoglu, Malek Hammou, Thomas de Lange, Sravanthi Parasa, Pål Halvorsen, Inga Strümke
Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for disease and other abnormalities.
no code implementations • 3 Mar 2022 • Thu Nguyen, Thanh Nhan Phan, Van Nhuong Nguyen, Thanh Binh Nguyen, Pål Halvorsen, Michael Riegler
The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison.
no code implementations • 24 Feb 2022 • Sharib Ali, Noha Ghatwary, Debesh Jha, Ece Isik-Polat, Gorkem Polat, Chen Yang, Wuyang Li, Adrian Galdran, Miguel-Ángel González Ballester, Vajira Thambawita, Steven Hicks, Sahadev Poudel, Sang-Woong Lee, Ziyi Jin, Tianyuan Gan, Chenghui Yu, Jiangpeng Yan, Doyeob Yeo, Hyunseok Lee, Nikhil Kumar Tomar, Mahmood Haithmi, Amr Ahmed, Michael A. Riegler, Christian Daul, Pål Halvorsen, Jens Rittscher, Osama E. Salem, Dominique Lamarque, Renato Cannizzaro, Stefano Realdon, Thomas de Lange, James E. East
Polyps are well-known cancer precursors identified by colonoscopy.
no code implementations • 2 Feb 2022 • Cise Midoglu, Steven A. Hicks, Vajira Thambawita, Tomas Kupka, Pål Halvorsen
This challenge aims to assist the automation of such a production pipeline using AI.
no code implementations • 20 Nov 2021 • Abhishek Srivastava, Sukalpa Chanda, Debesh Jha, Michael A. Riegler, Pål Halvorsen, Dag Johansen, Umapada Pal
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.
1 code implementation • Nordic Machine Intelligence 2021 • Steven Hicks, Debesh Jha, Vajira Thambawita, Pål Halvorsen, Bjørn-Jostein Singstad, Sachin Gaur, Klas Pettersen, Morten Goodwin, Sravanthi Parasa, Thomas de Lange, Michael Riegler
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.
no code implementations • 2 Sep 2021 • Andrea M. Storås, Inga Strümke, Michael A. Riegler, Jakob Grauslund, Hugo L. Hammer, Anis Yazidi, Pål Halvorsen, Kjell G. Gundersen, Tor P. Utheim, Catherine Jackson
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.
1 code implementation • 26 Jul 2021 • Debesh Jha, Pia H. Smedsrud, Dag Johansen, Thomas de Lange, Håvard D. Johansen, Pål Halvorsen, Michael A. Riegler
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
no code implementations • 5 Jul 2021 • Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Michael A. Riegler, Dag Johansen, Håvard D. Johansen, Pål Halvorsen
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
1 code implementation • 1 Jul 2021 • Vajira Thambawita, Steven A. Hicks, Pål Halvorsen, Michael A. Riegler
For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches.
4 code implementations • 29 Jun 2021 • Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal, Steven A. Hicks, Hugo L. Hammer, Sravanthi Parasa, Thomas de Lange, Pål Halvorsen, Michael A. Riegler
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.
3 code implementations • 8 Jun 2021 • Sharib Ali, Debesh Jha, Noha Ghatwary, Stefano Realdon, Renato Cannizzaro, Osama E. Salem, Dominique Lamarque, Christian Daul, Michael A. Riegler, Kim V. Anonsen, Andreas Petlund, Pål Halvorsen, Jens Rittscher, Thomas de Lange, James E. East
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists.
no code implementations • 6 Jun 2021 • Rabindra Khadga, Debesh Jha, Steven Hicks, Vajira Thambawita, Michael A. Riegler, Sharib Ali, Pål Halvorsen
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.
1 code implementation • 16 May 2021 • Abhishek Srivastava, Debesh Jha, Sukalpa Chanda, Umapada Pal, Håvard D. Johansen, Dag Johansen, Michael A. Riegler, Sharib Ali, Pål Halvorsen
The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets.
Ranked #5 on
Medical Image Segmentation
on 2018 Data Science Bowl
3 code implementations • 22 Apr 2021 • Debesh Jha, Nikhil Kumar Tomar, Sharib Ali, Michael A. Riegler, Håvard D. Johansen, Dag Johansen, Thomas de Lange, Pål Halvorsen
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
+4
1 code implementation • 31 Mar 2021 • Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler, Håvard D. Johansen, Dag Johansen, Jens Rittscher, Pål Halvorsen, Sharib Ali
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 #2 on
Medical Image Segmentation
on EM
1 code implementation • 6 Jan 2021 • Debesh Jha, Anis Yazidi, Michael A. Riegler, Dag Johansen, Håvard D. Johansen, Pål Halvorsen
Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks.
no code implementations • 30 Dec 2020 • Debesh Jha, Steven A. Hicks, Krister Emanuelsen, Håvard Johansen, Dag Johansen, Thomas de Lange, Michael A. Riegler, Pål Halvorsen
Colorectal cancer is the third most common cause of cancer worldwide.
1 code implementation • 30 Dec 2020 • Nikhil Kumar Tomar, Debesh Jha, Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael A. Riegler, Pål Halvorsen
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
1 code implementation • 14 Dec 2020 • Vajira Thambawita, Steven Hicks, Pål Halvorsen, Michael A. Riegler
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.
1 code implementation • 15 Nov 2020 • Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Håvard D. Johansen, Dag D. Johansen, Jens Rittscher, Michael A. Riegler, Pål Halvorsen
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
1 code implementation • 23 Oct 2020 • Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven A. Hicks, VajiraThambawita, Enrique Garcia-Ceja, Michael A. Riegler, Thomas de Lange, Peter T. Schmidt, Håvard D. Johansen, Dag Johansen, Pål Halvorsen
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
4 code implementations • 8 Jun 2020 • Debesh Jha, Michael A. Riegler, Dag Johansen, Pål Halvorsen, Håvard D. Johansen
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.
no code implementations • 8 May 2020 • Vajira Thambawita, Debesh Jha, Hugo Lewi Hammer, Håvard D. Johansen, Dag Johansen, Pål Halvorsen, Michael A. Riegler
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.
no code implementations • 23 Mar 2020 • Tobias Ross, Annika Reinke, Peter M. Full, Martin Wagner, Hannes Kenngott, Martin Apitz, Hellena Hempe, Diana Mindroc Filimon, Patrick Scholz, Thuy Nuong Tran, Pierangela Bruno, Pablo Arbeláez, Gui-Bin Bian, Sebastian Bodenstedt, Jon Lindström Bolmgren, Laura Bravo-Sánchez, Hua-Bin Chen, Cristina González, Dong Guo, Pål Halvorsen, Pheng-Ann Heng, Enes Hosgor, Zeng-Guang Hou, Fabian Isensee, Debesh Jha, Tingting Jiang, Yueming Jin, Kadir Kirtac, Sabrina Kletz, Stefan Leger, Zhixuan Li, Klaus H. Maier-Hein, Zhen-Liang Ni, Michael A. Riegler, Klaus Schoeffmann, Ruohua Shi, Stefanie Speidel, Michael Stenzel, Isabell Twick, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Wang, Yu-Jie Zhang, Yan-Jie Zhou, Lei Zhu, Manuel Wiesenfarth, Annette Kopp-Schneider, Beat P. Müller-Stich, Lena Maier-Hein
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.
no code implementations • 16 Nov 2019 • Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, Håvard D. Johansen
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 #7 on
Polyp Segmentation
on Kvasir-SEG
1 code implementation • 8 Nov 2019 • Vajira Thambawita, Pål Halvorsen, Hugo Hammer, Michael Riegler, Trine B. Haugen
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.
no code implementations • 8 Nov 2019 • Vajira Thambawita, Pål Halvorsen, Hugo Hammer, Michael Riegler, Trine B. Haugen
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.
no code implementations • 31 Oct 2018 • Vajira Thambawita, Debesh Jha, Michael Riegler, Pål Halvorsen, Hugo Lewi Hammer, Håvard D. Johansen, Dag Johansen
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract.