6 citations · 7 across the 10 of their papers we have counts for
8 papers · 1 filter
Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes
Daniel Reisenbüchler, Yousef Sadegheih, Michael Dittrich +3
The larval stage of Drosophila melanogaster is a compact model system for neuroscience whose genetic toolkit allows fluorescent markers to be expressed in defined neural population…
MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations
Leiyue Zhao, Tianyu Shi, Daniel Reisenbuchler +12
Instance-level quantification of kidney functional units is essential for morphometric analysis, yet most publicly available pathology datasets provide only semantic segmentation a…
Footprint-Guided Exemplar-Free Continual Histopathology Report Generation
Pratibha Kumari, Daniel Reisenbüchler, Afshin Bozorgpour +3
Rapid progress in vision-language modeling has enabled pathology report generation from gigapixel whole-slide images, but most approaches assume static training with simultaneous a…
Tissue Classification and Whole-Slide Images Analysis via Modeling of the Tumor Microenvironment and Biological Pathways
Junzhuo Liu, Xuemei Du, Daniel Reisenbuchler +5
Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studie…
From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology
Zhenhao Guo, Rachit Saluja, Tianyuan Yao +14
Fine-grained glomerular subtyping is essential for kidney biopsy interpretation, yet it presents a harder setting than the broad organ- or disease-level recognition tasks commonly…
CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation
Hongjin Fang, Daniel Reisenbüchler, Kenji Ikemura +3
Accurate segmentation of the glomerular basement membrane (GBM) in electron microscopy (EM) images is fundamental for quantifying membrane thickness and supporting the diagnosis of…
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West +28
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of micr…
Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction
Daniel Reisenbüchler, Sophia J. Wagner, Melanie Boxberg +1
Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich…