831 citations · 1.4k across the 28 of their papers we have counts for
6 papers · 1 filter
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra +5
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, pau…
Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet
Rupali Khatun, Soumick Chatterjee, Christoph Bert +6
Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously hea…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks
Domenico Iuso, Soumick Chatterjee, Sven Cornelissen +3
Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all…
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Soumick Chatterjee, Hana Haseljić, Robert Frysch +9
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm con…
Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN
Soumick Chatterjee, Pavan Tummala, Oliver Speck +1
Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most of these networks work with real-valued d…