Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Vajira Thambawita

Vajira Thambawita

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



Birk Torpmann-Hagen , Vajira Thambawita , Kyrre Glette , PĂ„l Halvorsen , Michael A. Riegler

Figure 1 for Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Figure 2 for Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Figure 3 for Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Figure 4 for Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
* 15 pages 
   Access Paper or Ask Questions

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps



Jan Andre Fagereng , Vajira Thambawita , Andrea M. StorÄs , Sravanthi Parasa , Thomas de Lange , PÄl Halvorsen , Michael A. Riegler

Figure 1 for PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Figure 2 for PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Figure 3 for PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Figure 4 for PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
* 6 pages 
   Access Paper or Ask Questions

Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos



Vladimir Monakhov , Vajira Thambawita , PĂ„l Halvorsen , Michael A. Riegler

Figure 1 for Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
Figure 2 for Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
Figure 3 for Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
Figure 4 for Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
* 7 pages 
   Access Paper or Ask Questions

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge



Sharib Ali , Noha Ghatwary , Debesh Jha , Ece Isik-Polat , Gorkem Polat , Chen Yang , Wuyang Li , Adrian Galdran , Miguel-Ángel Gonzålez 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 , PÄl Halvorsen , Jens Rittscher , Osama E. Salem , Dominique Lamarque , Renato Cannizzaro , Stefano Realdon , Thomas de Lange , James E. East

Figure 1 for Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Figure 2 for Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Figure 3 for Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Figure 4 for Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
* 26 pages 
   Access Paper or Ask Questions

MMSys'22 Grand Challenge on AI-based Video Production for Soccer



Cise Midoglu , Steven A. Hicks , Vajira Thambawita , Tomas Kupka , PĂ„l Halvorsen


   Access Paper or Ask Questions

DivergentNets: Medical Image Segmentation by Network Ensemble



Vajira Thambawita , Steven A. Hicks , PĂ„l Halvorsen , Michael A. Riegler

Figure 1 for DivergentNets: Medical Image Segmentation by Network Ensemble
Figure 2 for DivergentNets: Medical Image Segmentation by Network Ensemble
Figure 3 for DivergentNets: Medical Image Segmentation by Network Ensemble
Figure 4 for DivergentNets: Medical Image Segmentation by Network Ensemble
* Proceedings of the 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV 2021) colocated with with the 17th IEEE International Symposium on Biomedical Imaging (ISBI 2021) 
 * the winning model of the segmentation generalization challenge at EndoCV 2021 
   Access Paper or Ask Questions

SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation



Vajira Thambawita , Pegah Salehi , Sajad Amouei Sheshkal , Steven A. Hicks , Hugo L. Hammer , Sravanthi Parasa , Thomas de Lange , PĂ„l Halvorsen , Michael A. Riegler

Figure 1 for SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation
Figure 2 for SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation
Figure 3 for SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation
Figure 4 for SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation

   Access Paper or Ask Questions

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



Rabindra Khadga , Debesh Jha , Sharib Ali , Steven Hicks , Vajira Thambawita , Michael A. Riegler , PĂ„l Halvorsen

Figure 1 for Few-shot segmentation of medical images based on meta-learning with implicit gradients
Figure 2 for Few-shot segmentation of medical images based on meta-learning with implicit gradients
Figure 3 for Few-shot segmentation of medical images based on meta-learning with implicit gradients
Figure 4 for Few-shot segmentation of medical images based on meta-learning with implicit gradients

   Access Paper or Ask Questions

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



Vajira Thambawita , Steven Hicks , PĂ„l Halvorsen , Michael A. Riegler

Figure 1 for Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Figure 2 for Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Figure 3 for Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus

   Access Paper or Ask Questions

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 , HĂ„vard D. Johansen , Dag Johansen , PĂ„l Halvorsen , Michael A. Riegler

Figure 1 for An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
Figure 2 for An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
Figure 3 for An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
Figure 4 for An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
* 30 pages, 12 figures, 8 tables, Accepted for ACM Transactions on Computing for Healthcare 
   Access Paper or Ask Questions
1
2
>>