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mahesh shakya

maheshshakya.01

  •  Nepal
  •  Nepal Applied Mathematics and Informatics Institute for Research, NAAMII
  •  Biomedical Image Analysis
  •  Website
Statistics
  • Member for 5 years, 11 months
  • 2 challenge submissions
  • 4 algorithms run

Activity Overview

CRASS Logo
CRASS
Challenge User

CRASS stands for Chest Radiograph Anatomical Structure Segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.

LUNA16 Logo
LUNA16
Challenge User

The LUNA16 challenge: automatic nodule detection on chest CT

CATARACTS Logo
CATARACTS
Challenge User

The Challenge on Automatic Tool Annotation for cataRACT Surgery aims at evaluating image-based tool detection algorithms in the context of the most common surgical procedure in the world.

IDRiD Logo
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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DRIVE
Challenge User

Develop a system to automatically segment vessels in human retina fundus images.

HC18 Logo
HC18
Challenge User

Automated measurement of fetal head circumference using 2D ultrasound images

CHAOS Logo
CHAOS
Challenge User

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

PALM Logo
PALM
Challenge User

The Pathologic Myopia Challenge (PALM) focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients.

EndoVisSub2019-SCARED Logo
Stereo Correspondence and Reconstruction of Endoscopic Data
Challenge User

Stereo Correspondence and Reconstruction of Endoscopic Data

AMD Logo
iChallenge-AMD
Challenge User

Age-related Macular Degeneration Challenge

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ODIR-2019
Challenge User

北京大学国际眼底图像智能识别竞赛 Peking University International Competition on Ocular Disease Intelligent Recognition

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RibFrac
Challenge User

Rib Fracture Detection and Classification Challenge: A large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80Gb)

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QUBIQ
Challenge User

Quantification of Uncertainties in Biomedical Image Segmentation Challenge

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Apples-CT
Challenge User

High-throughput CT image reconstruction and defect detection for apples

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FeTA - Fetal Tissue Annotation Challenge
Challenge User

Fetal Tissue Annotation Challenge

AutoImplant2021 Logo
AutoImplant 2021
Challenge User

Second MICCAI Challenge on Automatic Cranial Implant Design (AutoImplant 2021)

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DFUC2021
Challenge User

Diabetic Foot Ulcer Challenge 2021

NODE21 Logo
NODE21
Challenge User

NODE21: generate and detect nodules on chest radiographs

QUBIQ21 Logo
QUBIQ2021
Challenge User

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 2021

EndoCV2022 Logo
EndoCV2022
Challenge User

Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data

RAVIR Logo
RAVIR
Challenge User

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

SynthRAD2023 Logo
SynthRAD2023
Challenge User

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

3DTeethSeg Logo
3D Teeth Scan Segmentation and Labeling Challenge MICCAI2022
Challenge User

Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and therefore ease the prediction of treatment outcomes. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labeling available in the literature and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws.

DRAC22 Logo
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

ENDO-AID Logo
Endometrial Carcinoma Detection in Pipelle biopsies
Challenge User

Evaluation platform as reference benchmark for algorithms that can predict endometrial carcinoma on whole-slide images of Pipelle sampled endometrial slides stained in H&E, based on the test data set used in our project.

MedFM2023 Logo
Foundation Model Prompting for Medical Image Classification
Challenge User

The primary objective of this challenge is to promote the development and evaluation of model adaptation techniques for medical image classification to leverage the existing foundation models.

DENTEX Logo
DENTEX - MICCAI23
Challenge User

Dental Enumeration and Diagnosis on Panoramic X- rays Challenge

THOMPSON-Challenge Logo
The Trauma THOMPSON Challenge
Challenge User

ACOUSLIC-AI Logo
Abdominal Circumference Operator-agnostic UltraSound measurement
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.

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Pelvic Bone Fragments with Injuries Segmentation Challenge
Challenge User

Pelvic fracture segmentation in CT and X-ray

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Challenge Hosting Masterclass
Challenge User

A hands-on workshop designed to provide participants with practical experience in setting up a challenge.