Search Results for author:
Found 6 papers, 3 papers with code
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation using Deformable Shape Models
In this work, we introduce a globally optimal approach based on deformable shape models and global energy minimization for cell nuclei segmentation and cluster splitting.
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection
We propose a novel hybrid CNN-ViT model for cell detection in microscopy images to exploit the advantages of both types of deep learning models.
EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring
Since the number of available 3D microscopy images is typically limited in applications, we take a different approach and introduce a small CNN for volumetric cell segmentation.
Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data
We introduce a new method for registration and 3D reconstruction of high- and ultra-high resolution (64 $\mu$m and 1. 3 $\mu$m pixel size) histological images of a Wistar rat brain acquired by 3D polarized light imaging (3D-PLI).
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Automatic breast cancer grading in lymph nodes using a deep neural network
We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading.
