Search Results for author: Pål Halvorsen

Found 23 papers, 12 papers with code

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.

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.

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

We show that these synthetic data generation pipelines can be used as an alternative to bypass privacy concerns and as an alternative way to produce artificial segmentation datasets with corresponding ground truth masks to avoid the tedious medical data annotation process.

Medical Image Segmentation Synthetic Data Generation

Few-shot segmentation of medical images based on meta-learning with implicit gradients

no code implementations6 Jun 2021 Rabindra Khadga, Debesh Jha, Sharib Ali, Steven Hicks, Vajira Thambawita, Michael A. Riegler, Pål Halvorsen

Classical supervised methods commonly used often suffer from the requirement of an abudant number of training samples and are unable to generalize on unseen datasets.

Few-Shot Learning Medical Image Segmentation

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

no code implementations31 Mar 2021 Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler, Håvard D. Johansen, Dag Johansen, Jens Rittscher, Pål Halvorsen, Sharib Ali

With the increase in available large clinical and experimental datasets, there has been substantial amount of work being done on addressing the challenges in the area of biomedical image analysis.

Medical Image Segmentation

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.

Medical Image Segmentation

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

1 code implementation8 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 +3

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)

Medical Image Segmentation Polyp Segmentation

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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