Latest News

Researchers from Satsuma Lab have introduced a hybrid approach combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to predict long-term survival in a Lung Cancer Screening (LCS) study. It was demonstrated that incorporating the patient’s imaging follow-up history can lead to improvement in survival prediction.

Satsuma Lab is delighted to announce that Moucheng was named a finalist for the MICCAI Young Scientist Award for his paper: Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation (Semi-Supervised Segmentation with Pseudo Labels).

Satsuma Lab had a great time at the Centre for Medical Imaging (CMIC) away day! Spending two days engaging with fellow medical image Core and labs in the centre. Thank you to all for a great event.

Satsuma Lab presenting two posters at the special BMVA symposium 2022 in Manchester, UK! Checkout our posters on: Learning Morphological Feature Perturbation for Semi-Supervised Segmentation Unsupervised airway measurement to predict survival in bronchiectasis

Cystic Fibrosis is one of the key diseases that we’re working towards understanding with CT imaging here in the Satsuma Lab. Check out this video

After 3 active years, the Satsuma research group based within the Centre for Medical Image Computing at University College London has its very own website.