5 papers
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…
LocBAM: Advancing 3D Patch-Based Image Segmentation by Integrating Location Contex
Donnate Hooft, Stefan M. Fischer, Cosmin Bercea +2
Patch-based methods are widely used in 3D medical image segmentation to address memory constraints in processing high-resolution volumetric data. However, these approaches often ne…
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…
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…
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Reuben Dorent, Roya Khajavi, Tagwa Idris +24
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentati…