2 citations · 3 across the 5 of their papers we have counts for
5 papers
Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
Christian Gapp, Elias Tappeiner, Martin Welk +8
Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes models to over-rely on domina…
SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Jia Fu, Litingyu Wang, He Li +27
Accurate delineation of Gross Tumor Volume (GTV), Lymph Node Clinical Target Volume (LN CTV), and Organ-at-Risk (OAR) from Computed Tomography (CT) scans is essential for precise r…
What are You Looking at? Modality Contribution in Multimodal Medical Deep Learning
Christian Gapp, Elias Tappeiner, Martin Welk +3
Purpose High dimensional, multimodal data can nowadays be analyzed by huge deep neural networks with little effort. Several fusion methods for bringing together different modalitie…
Multimodal Medical Disease Classification with LLaMA II
Christian Gapp, Elias Tappeiner, Martin Welk +1
Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrat…
Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation
Elias Tappeiner, Martin Welk, Rainer Schubert
The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image guided radiation therapy. The automation of the segmentation task is therefo…