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Tom Au

ocb00999

  •  Australia
  •  Macquarie University
  •  Biomedical Science
Statistics
  • Member for 4 years, 9 months
  • 39 challenge submissions
  • 26 algorithms run

Activity Overview

CAMELYON17 Logo
CAMELYON17
Challenge User

Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.

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RETOUCH
Challenge User

Retinal OCT Fluid Challenge (RETOUCH) compares automated algorithms that are able to detect and segment different types of retinal fluid in optical coherence tomography (OCT).

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ROCC
Challenge User

Retinal OCT Classification Challenge (ROCC) is organized as a one day Challenge in conjunction with MVIP2017. The goal of this challenge is to call different automated algorithms that are able to detect DR disease from normal retina on a common dataset of OCT volumes, acquired with Topcon SD-OCT devices.

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IDRiD
Challenge User

This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.

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REFUGE
Challenge User

The goal of the Retinal Fundus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.

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BreastPathQ: Cancer Cellularity Challenge 2019
Challenge User

SPIE-AAPM-NCI BreastPathQ:Cancer Circularity Challenge 2019: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity for tumor burden assessment in each.

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EAD2019
Challenge User

Endoscopic Artefact Detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) Multi-class artefact detection, 2) Region segmentation, 3) Detection generalisation.

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KiTS19
Challenge User

2019 Kidney and Kidney Tumor Segmentation Challenge

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iChallenge-AMD
Challenge User

Age-related Macular Degeneration Challenge

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EndoCV2020
Challenge User

Endoscopy computer vision challenge (EndoCV2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).

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CADA
Challenge User

Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (SAH) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.

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CADA - Rupture Risk Estimation
Challenge User

Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (SAH) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.

CADA-AS Logo
CADA - Aneurysm Segmentation
Challenge User

Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (SAH) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.

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RIADD (ISBI-2021)
Challenge User

Retinal Image Analysis for multi-Disease Detection

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EndoCV2021
Challenge User

Endoscopy Computer Vision Challenge 2021

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WSSS4LUAD
Challenge User

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

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TIGER
Challenge User

Grand challenge on automate assessment of tumor infiltrating lymphocytes in digital pathology slides of triple negative and Her2-positive breast cancers

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BCNB
Challenge User

Early Breast Cancer Core-Needle Biopsy WSI Dataset

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RAVIR
Challenge User

A dataset for semantic segmentation and quantitative analysis of retinal arteries and veins in infrared reflectance imaging

autoPET Logo
autoPET
Challenge User

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

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Diabetic Retinopathy Analysis Challenge MICCAI2022
Challenge User

Diabetic Retinopathy (DR) lesions segmentation, image quality assessment and classification of proliferatived DR (PDR) and non-PDR in ultra-wide optical coherence tomography angiography mosaic (UW-OCTA-M) images

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

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Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
Challenge User

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Ischemic Stroke Lesion Segmentation Challenge
Challenge User

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autoPET-II
Challenge User

Automated Lesion Segmentation in PET/CT - Domain Generalization

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ARCADE-MICCAI2023
Challenge User

UltrasoundEnhance2023 Logo
Ultrasound Image Enhancement challenge 2023
Challenge User

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

LEOPARD Logo
The LEOPARD Challenge
Challenge User

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AutoPET III
Challenge User

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Ischemic Stroke Lesion Segmentation Challenge 2024
Challenge User

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TopCoW 2024 Challenge
Challenge User

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

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MONKEY challenge: Detection of inflammation in kidney biopsies
Challenge User

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

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The LUNA25 Challenge
Challenge User

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REport Generation in pathology using Pan-Asia Giga-pixel WSIs
Challenge User

This project focuses on advancing automated pathology report generation using vision-language foundation models. It addresses the limitations of traditional NLP metrics (e.g., BLEU, METEOR, ROUGE) by emphasizing clinically relevant evaluation. The initiative includes standardized datasets, expert comparisons, and medical-domain-specific metrics to assess model performance. It also explores the integration of generated reports into diagnostic workflows with clinical feedback. To support fairness and generalizability, the challenge dataset comprises ~20,500 cases from six medical centers in Korea, Japan, India, Turkey, and Germany, promoting multicultural and multiethnic medical AI development.

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ATLAS_UNET
Algorithm User