5 papers
Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
Maximilian Tschuchnig, Michael Gadermayr, Khalifa Djemal
Automated breast cancer classification from mammography remains a significant challenge due to subtle distinctions between benign and malignant tissue. In this work, we present a h…
Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
Maximilian Tschuchnig, Lukas Lamminger, Philipp Steininger +1
Cone-Beam Computed Tomography (CBCT) is widely used for intraoperative imaging due to its rapid acquisition and low radiation dose. However, CBCT images typically suffer from artif…
Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment
Maximilian Tschuchnig, Lukas Lamminger, Philipp Steininger +1
Cone-Beam Computed Tomography (CBCT) is widely used for real-time intraoperative imaging due to its low radiation dose and high acquisition speed. However, despite its high resolut…
Initial Study On Improving Segmentation By Combining Preoperative CT And Intraoperative CBCT Using Synthetic Data
Maximilian E. Tschuchnig, Philipp Steininger, Michael Gadermayr
Computer-Assisted Interventions enable clinicians to perform precise, minimally invasive procedures, often relying on advanced imaging methods. Cone-beam computed tomography (CBCT)…
Virtually Objective Quantification of in vitro Wound Healing Scratch Assays with the Segment Anything Model
Katja Löwenstein, Johanna Rehrl, Anja Schuster +1
The in vitro scratch assay is a widely used assay in cell biology to assess the rate of wound closure related to a variety of therapeutic interventions. While manual measurement is…