activity
20242026
most citedMask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge

4 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction

Johannes Kiechle, Richard Osuala, Daniel M. Lang +5

In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated…

eess.IV2025

TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks

Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang +5

The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downstream tasks such as tumor chara…

cs.CV2025

Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation

Stefan M. Fischer, Johannes Kiechle, Laura Daza +6

In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the…

cs.CV2024★ 2 cited

Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?

Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer +3

Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data.…

cs.CV2024

Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks

Stefan M. Fischer, Lina Felsner, Richard Osuala +4

In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defi…

cs.CV2024★ 4 cited

Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge

Stefan M. Fischer, Johannes Kiechle, Daniel M. Lang +2

Pathological lymph node delineation is crucial in cancer diagnosis, progression assessment, and treatment planning. The MICCAI 2023 Lymph Node Quantification Challenge published th…