saad's profile

Saad Ullah Akram

saad

Finland
Aalto University and MVision AI
CSE
Website

Statistics

Member for 9 years, 10 months

Activity Overview

MITOS-ATYPIA-14

Challenge User

MITOS & ATYPIA 14 Contest, hosted by conference ICPR 2014Detection of mitosis and evaluation of nuclear atypia on breast cancer H&E stained images

PROMISE12

Challenge User

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

LUNA16

Challenge User

The LUNA16 challenge: automatic nodule detection on chest CT

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.

ICIAR 2018

Challenge User

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

SLIVER07

Challenge User

The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.

CHAOS

Challenge User

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

Decathlon

Challenge User

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

KiTS19

Challenge User

2019 Kidney and Kidney Tumor Segmentation Challenge

PAIP 2019

Challenge User

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

VerSe`19

Challenge User

Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.

StructSeg2019

Challenge User

Welcome to Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

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

LNDb Challenge

Challenge User

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

VerSe'20

Challenge User

Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.

RibFrac

Challenge User

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

Learn2Reg

Challenge User

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

FLARE21

Challenge User

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

The PI-CAI Challenge

Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

SynthRAD2023

Challenge User

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

MICCAI FLARE 2022

Challenge User

MICCAI 2022 Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation (FLARE) Challenge

ToothFairy: Cone-Beam Computed Tomography Segmentation Challenge

Challenge User

This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Raudboud University. This challenge aims at pushing the development of deep learning frameworks to segment the Inferior Alveolar Canal (IAC) by incrementally extending the amount of publicly available 3D-annotated Cone Beam Computed Tomography (CBCT) scans. CBCT modality is becoming increasingly important for treatment planning and diagnosis in implant dentistry and maxillofacial surgery. The three-dimensional information acquired with CBCT can be crucial to plan a vast number of surgical interventions with the aim of preserving noble anatomical structures such as the Inferior Alveolar Canal (IAC), which contains the homonymous nerve (Inferior Alveolar Nerve, IAN). Deep learning models can support medical personnel in surgical planning procedures by providing a voxel-level segmentation of the IAN automatically extracted from CBCT scans.

SEG.A. - Segmentation of the Aorta

Challenge User

Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.

SegRap 2023

Challenge User

A segmentation challenge with 200 patients (two modalities of CT images, 45 OARs and 2 GTVs).