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Toan Dinh Minh

mtoan65

  •  Vietnam
  •  Da Nang University of Science and Technology
  •  Information Technology Faculty
Statistics
  • Member for 6 months, 2 weeks
  • 15 challenge submissions

Activity Overview

CHAOS Logo
CHAOS
Challenge User

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

PI-CAI Logo
The PI-CAI Challenge
Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

PANORAMA Logo
PANORAMA
Challenge User

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

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

LEOPARD Logo
The LEOPARD Challenge
Challenge User

AutoPET-III Logo
AutoPET III
Challenge User

ToothFairy2 Logo
ToothFairy2: Multi-Structure Segmentation in CBCT Volumes
Challenge User

This is the second edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Radboud University Medical Center. The challenge is hosted by grand-challenge and is part of MICCAI2024.

PENGWIN Logo
Pelvic Bone Fragments with Injuries Segmentation Challenge
Challenge User

Pelvic fracture segmentation in CT and X-ray

topcow24 Logo
TopCoW 2024 Challenge
Challenge User

Segment, classify, and detect the Circle of Willis (CoW) for both CTA and MRA

SELMA3D Logo
Self-supervised learning for 3D light-sheet microscopy image seg
Challenge User

BONBID-HIE2024 Logo
2nd BONBID-HIE Challenge for HIE Outcome Prediction and Lesion S
Challenge User

Lung nodule detection for routine clinical CT scans Logo
Lung nodule detection for routine clinical CT scans
Algorithm User

Deep learning for the detection of pulmonary nodules in chest CT scans

HeviAI (A. Karagoz, et al.; Turkey) algorithm trained on PI-CAI: Private and Public Training Dataset Logo
HeviAI (A. Karagoz, et al.; Turkey) algorithm trained on PI-CAI: Private and Public Training Dataset
Algorithm User

PI-CAI: Baseline nnU-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset Logo
PI-CAI: Baseline nnU-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset
Algorithm User

PI-CAI: Baseline nnDetection (semi-supervised) trained on PI-CAI: Private and Public Training Dataset Logo
PI-CAI: Baseline nnDetection (semi-supervised) trained on PI-CAI: Private and Public Training Dataset
Algorithm User

BDAV_Y (Y. Yuan, et al.; Australia) algorithm trained on PI-CAI: Private and Public Training Dataset Logo
BDAV_Y (Y. Yuan, et al.; Australia) algorithm trained on PI-CAI: Private and Public Training Dataset
Algorithm User

Swangeese (H. Kan, et al.; China) algorithm trained on PI-CAI: Private and Public Training Dataset Logo
Swangeese (H. Kan, et al.; China) algorithm trained on PI-CAI: Private and Public Training Dataset
Algorithm User

PIMed-Stanford (X. Li, S. Vesal, S. Saunders, et al.; USA) algorithm trained on PI-CAI: Private and Public Training Dataset Logo
PIMed-Stanford (X. Li, S. Vesal, S. Saunders, et al.; USA) algorithm trained on PI-CAI: Private and Public Training Dataset
Algorithm User

PI-CAI: Baseline U-Net (supervised) trained on PI-CAI: Private and Public Training Dataset Logo
PI-CAI: Baseline U-Net (supervised) trained on PI-CAI: Private and Public Training Dataset
Algorithm User

PI-CAI: Baseline U-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset Logo
PI-CAI: Baseline U-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset
Algorithm User

PI-CAI: Baseline nnDetection (supervised) trained on PI-CAI Private and Public Training Dataset Logo
PI-CAI: Baseline nnDetection (supervised) trained on PI-CAI Private and Public Training Dataset
Algorithm User

PI-CAI: Baseline nnDetection (supervised) trained on PI-CAI Private and Public Training Dataset