101 citations · 117 across the 16 of their papers we have counts for
11 papers · 1 filter
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)…
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation
Maximilian E. Tschuchnig, Philipp Steininger, Michael Gadermayr
Cone-beam computed tomography (CBCT) is an important tool facilitating computer aided interventions, despite often suffering from artifacts that pose challenges for accurate interp…
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction
Maximilian Ernst Tschuchnig, Julia Coste-Marin, Philipp Steininger +1
Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentati…
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
Maximilian E. Tschuchnig, Philipp Grubmüller, Lea M. Stangassinger +5
Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods ar…
Anomaly Detection in Medical Imaging -- A Mini Review
Maximilian E. Tschuchnig, Michael Gadermayr
The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts…
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
Michael Gadermayr, Maximilian Tschuchnig, Lea Maria Stangassinger +4
In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during th…
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
Maximilian Ernst Tschuchnig, Gertie Janneke Oostingh, Michael Gadermayr
Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole slide scanners enables the fast acquisition of large amount…
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta +4
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medica…