collaborators

6 papers

eess.IV2025

GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation

Tomasz Szczepański, Szymon Płotka, Michal K. Grzeszczyk +5

Tooth segmentation in Cone-Beam Computed Tomography (CBCT) remains challenging, especially for fine structures like root apices, which is critical for assessing root resorption in…

cs.RO2025

FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning

Diego Dall'Alba, Michał Naskręt, Sabina Kaminska +1

Robotic surgery is a rapidly developing field that can greatly benefit from the automation of surgical tasks. However, training techniques such as Reinforcement Learning (RL) requi…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

eess.IV2024

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…