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Tassilo Wald

TWald

  •  Germany
  •  Deutsches Krebs Forschungs Zentrum (DKFZ)
  •  Medical Imaging Computing
  •  Website
Statistics
  • Member for 4 years, 6 months
  • 5 challenge submissions

Activity Overview

VALDO Logo
Where is VALDO?
Challenge User

Vascular Lesion Detection Challenge at MICCAI 2021

SegPC-2021 Logo
SegPC-2021
Challenge User

This challenge is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer.

BrainPTM-2021 Logo
BrainPTM 2021
Challenge User

Brain Pre-surgical Tractography Mapping (BrainPTM) in real clinical scans.

CoNIC-Challenge Logo
CoNIC 2022
Challenge User

Colon Nuclei Identification and Counting Challenge 2022

ATLAS Logo
ATLAS R2.0 - Stroke Lesion Segmentation
Challenge User

Anatomical Tracings of Lesions After Stroke

autoPET Logo
autoPET
Challenge User

Automatic lesion segmentation in whole-body FDG-PET/CT

AMOS22 Logo
Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022
Challenge User

vessel-wall-segmentation-2022 Logo
Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
Challenge User

crossmoda2022 Logo
Cross-Modality Domain Adaptation: Segmentation & Classification
Challenge User

The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

shifts Logo
Shifts Challenge 2022
Challenge User

The goal of the Shifts Challenge 2022 is to raise awareness among the research community about the problems of distributional shift, robustness, and uncertainty estimation, and to identify new solutions to address them. The competition will consist of two new tracks: White Matter Multiple Sclerosis (MS) lesion segmentation in 3D Magnetic Resonance Imaging (MRI) of the brain and Marine cargo vessel power estimation.

isles22 Logo
Ischemic Stroke Lesion Segmentation Challenge
Challenge User

HaN-Seg2023 Logo
The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge
Challenge User

A semantic multimodal segmentation challenge comprising 30 organs at risk

LNQ2023 Logo
LNQ2023
Challenge User

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.

BONBID-HIE2023 Logo
Hypoxic Ischemic Encephalopathy Lesion Segmentation Challenge
Challenge User

HNTSMRG24 Logo
Head and Neck Tumor Segmentation for MR-Guided Applications
Challenge User

This challenge focuses on developing algorithms to automatically segment head and neck cancer gross tumor volumes on multi-timepoint MRI

ACOUSLIC-AI Logo
Abdominal Circumference Operator-agnostic UltraSound measurement
Challenge User

ISLES-24 Logo
Ischemic Stroke Lesion Segmentation Challenge 2024
Challenge User

AIMS-TBI Logo
Automated Identification of Mod-Sev TBI Lesions
Challenge User