Search Results for author: Vajira Thambawita

Found 12 papers, 4 papers with code

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.

Medical Image Segmentation Semantic Segmentation +2

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 Medical Image Segmentation +1

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 Semantic Segmentation

Real-Time Detection of Events in Soccer Videosusing 3D Convolutional Neural Networks

no code implementations2 Dec 2020 Olav A. Nergard Rongved, Steven A. Hicks, Vajira Thambawita, Hakon K. Stensland, Evi Zouganeli, Dag Johansen, Michael A. Riegler, Pal Halvorsen

The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted.

Action Spotting

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.

Frame

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

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