Search Results for author: Pål Halvorsen

Found 38 papers, 21 papers with code

VISEM-Tracking: Human Spermatozoa Tracking Dataset

1 code implementation6 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.

Combining datasets to increase the number of samples and improve model fitting

no code implementations11 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.

Imputation Time Series +1

Towards the Neuroevolution of Low-level Artificial General Intelligence

1 code implementation27 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.

Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation

1 code implementation30 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.

Data Augmentation Image Segmentation +3

Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos

1 code implementation30 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.

Anomaly Detection Video Anomaly Detection

Predicting tacrolimus exposure in kidney transplanted patients using machine learning

no code implementations9 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.

BIG-bench Machine Learning

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations

no code implementations23 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.

Explainable artificial intelligence

Parallel feature selection based on the trace ratio criterion

no code implementations3 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.


PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation

no code implementations20 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.

Decision Making Image Segmentation +2

MedAI: Transparency in Medical Image Segmentation

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.

Image Segmentation Medical Image Segmentation +1

Artificial Intelligence in Dry Eye Disease

no code implementations2 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.

A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation

1 code implementation26 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-ColonDB (using extra training data)

Medical Image Segmentation

Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy

no code implementations5 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.

Medical Image Segmentation

SinGAN-Seg: Synthetic training data generation for medical image segmentation

1 code implementation29 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.

Image Segmentation Medical Image Segmentation +3

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

no code implementations6 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.

Few-Shot Learning Image Segmentation +2

NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

1 code implementation22 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.

Colorectal Polyps Characterization Instrument Recognition +3

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

1 code implementation31 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.

Hard Attention Image Segmentation +2

Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus

1 code implementation14 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.

Image Segmentation Medical Image Segmentation +1

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

3 code implementations8 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.

Cell Segmentation Colorectal Polyps Characterization +5

Kvasir-SEG: A Segmented Polyp Dataset

no code implementations16 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 #1 on Polyp Segmentation on Kvasir-SEG (DSC metric)

Image Segmentation Medical Image Segmentation +1

Extracting temporal features into a spatial domain using autoencoders for sperm video analysis

1 code implementation8 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.

Transfer Learning

Stacked dense optical flows and dropout layers to predict sperm motility and morphology

no code implementations8 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.


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