6 citations · 13 across the 10 of their papers we have counts for
5 papers · 1 filter
SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models
Sabina Martyniak, Joanna Kaleta, Diego Dall'Alba +3
Computer-assisted surgical (CAS) systems enhance surgical execution and outcomes by providing advanced support to surgeons. These systems often rely on deep learning models trained…
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
Joanna Kaleta, Paweł Skierś, Jan Dubiński +2
We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique chal…
PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz +3
Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstructi…
TabMixer: Noninvasive Estimation of the Mean Pulmonary Artery Pressure via Imaging and Tabular Data Mixing
Michal K. Grzeszczyk, Przemysław Korzeniowski, Samer Alabed +3
Right Heart Catheterization is a gold standard procedure for diagnosing Pulmonary Hypertension by measuring mean Pulmonary Artery Pressure (mPAP). It is invasive, costly, time-cons…
Let Me DeCode You: Decoder Conditioning with Tabular Data
Tomasz Szczepański, Michal K. Grzeszczyk, Szymon Płotka +5
Training deep neural networks for 3D segmentation tasks can be challenging, often requiring efficient and effective strategies to improve model performance. In this study, we intro…