10 citations · 22 across the 8 of their papers we have counts for
7 papers · 1 filter
Towards Hierarchical Regional Transformer-based Multiple Instance Learning
Josef Cersovsky, Sadegh Mohammadi, Dagmar Kainmueller +1
The classification of gigapixel histopathology images with deep multiple instance learning models has become a critical task in digital pathology and precision medicine. In this wo…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
How Shift Equivariance Impacts Metric Learning for Instance Segmentation
Josef Lorenz Rumberger, Xiaoyan Yu, Peter Hirsch +6
Metric learning has received conflicting assessments concerning its suitability for solving instance segmentation tasks. It has been dismissed as theoretically flawed due to the sh…
An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images
Peter Hirsch, Dagmar Kainmueller
Segmentation of cell nuclei in microscopy images is a prevalent necessity in cell biology. Especially for three-dimensional datasets, manual segmentation is prohibitively time-cons…
PatchPerPix for Instance Segmentation
Peter Hirsch, Lisa Mais, Dagmar Kainmueller
We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with…
Convexity Shape Constraints for Image Segmentation
Loic A. Royer, David L. Richmond, Carsten Rother +2
Segmenting an image into multiple components is a central task in computer vision. In many practical scenarios, prior knowledge about plausible components is available. Incorporati…
Mapping Auto-context Decision Forests to Deep ConvNets for Semantic Segmentation
David L. Richmond, Dagmar Kainmueller, Michael Y. Yang +2
We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Decision Fo…