Search Results for author:
Found 6 papers, 2 papers with code
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images.
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
In this paper, we propose a weakly supervised nuclei segmentation method, which requires only point annotations for training.
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.
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
Recent advances in convolutional neural networks (CNN) have shown significant improvements in histological slide analysis, but accuracy is not optimized because of the difficulty in the handling of gigapixel images.
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images.
Action-Driven Object Detection with Top-Down Visual Attentions
In this paper, we present an "action-driven" detection mechanism using our "top-down" visual attention model.
