- Grand Challenge

Eric Upschulte

ericup

Germany
Research Centre Jülich
Institute of Neuroscience and Medicine

Statistics

Member for 2 years, 3 months
37 challenge submissions

Activity Overview

CoNIC 2022

Challenge User

Colon Nuclei Identification and Counting Challenge 2022

Cell Segmentation in Multi-modality Microscopy Images

Challenge User

Weakly Supervised Cell Segmentation in Multi-modality High-resolution Microscopy Images

MONKEY challenge: Detection of inflammation in kidney biopsies

Challenge User

MONKEY (Machine-learning for Optimal detection of iNflammatory cells in KidnEY)

PUMA: Panoptic segmentation of nUclei and tissue in MelanomA

Challenge User

The PUMA Challenge aims to enhance nuclei and tissue segmentation in melanoma histopathology, addressing the need for better prognostic biomarkers to predict treatment responses. Melanoma, a highly aggressive skin cancer, often requires immune checkpoint inhibition therapy, but only half of patients respond. Prognostic biomarkers like tumor infiltrating lymphocytes (TILs) correlate with better therapy responses and lower recurrence rate, but manual TIL scoring is subjective and inconsistent. Current deep learning methods underperform. The PUMA dataset includes annotated primary and metastatic melanoma regions to improve segmentation techniques. The challenge includes two tracks with tasks focused on tissue and nuclei segmentation, encouraging advanced methods to improve predictive accuracy.