no code implementations • 25 Apr 2024 • Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh
We evaluate our proposed method on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) dataset, where our method outperforms state-of-the-art methods in segmenting cardiac regions of interest in both short-axis and long-axis images.
no code implementations • 6 Apr 2024 • Muhammad Asad, Ihsan Ullah, Ganesh Sistu, Michael G. Madden
The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution.
1 code implementation • 14 Feb 2024 • Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation.
no code implementations • 5 Sep 2023 • Helena Williams, João Pedrosa, Muhammad Asad, Laura Cattani, Tom Vercauteren, Jan Deprest, Jan D'hooge
Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0. 00001)
1 code implementation • 18 May 2023 • Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle, Muhammad Asad, Richard Brown, Vishwesh Nath, Alvin Ihsani, Michela Antonelli, Daniel Palkovics, Csaba Pinter, Ron Alkalay, Steve Pieper, Holger R. Roth, Daguang Xu, Prerna Dogra, Tom Vercauteren, Andrew Feng, Abood Quraini, Sebastien Ourselin, M. Jorge Cardoso
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
1 code implementation • 23 Mar 2023 • Muhammad Asad, Helena Williams, Indrajeet Mandal, Sarim Ather, Jan Deprest, Jan D'hooge, Tom Vercauteren
In this work, we propose an adaptive multi-scale online likelihood network (MONet) that adaptively learns in a data-efficient online setting from both an initial automatic segmentation and user interactions providing corrections.
no code implementations • 21 Feb 2023 • Peichao Li, Muhammad Asad, Conor Horgan, Oscar MacCormac, Jonathan Shapey, Tom Vercauteren
However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images.
1 code implementation • journal 2022 • MIAN MUHAMMAD SADIQ FAREED, SHAHID ZIKRIA, GULNAZ AHMED, MUI-ZZUD-DIN, SAQIB MAHMOOD, Muhammad Aslam, SYEDA FIZZAH JILLANI, AHMAD MOUSTAFA, Muhammad Asad
This study aims to create a reliable and ef cient system for classifying AD using MRI by applying the deep Convolutional Neural Network (CNN).
no code implementations • 9 Aug 2022 • Navodini Wijethilake, Aaron Kujawa, Reuben Dorent, Muhammad Asad, Anna Oviedova, Tom Vercauteren, Jonathan Shapey
It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal.
1 code implementation • 26 Jul 2022 • Muhammad Asad, Reuben Dorent, Tom Vercauteren
The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware.
2 code implementations • 23 Mar 2022 • Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath, Yucheng Tang, Alvin Ihsani, Muhammad Asad, Fernando Pérez-García, Pritesh Mehta, Wenqi Li, Mona Flores, Holger R. Roth, Tom Vercauteren, Daguang Xu, Prerna Dogra, Sebastien Ourselin, Andrew Feng, M. Jorge Cardoso
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
2 code implementations • 12 Jan 2022 • Muhammad Asad, Lucas Fidon, Tom Vercauteren
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes.
no code implementations • 22 Jul 2021 • Muhammad Asad, Ahmed Moustafa, Takayuki Ito
Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server.
no code implementations • 6 Apr 2020 • Muhammad Asad, Ahmed Moustafa, Takayuki Ito, Muhammad Aslam
These amounts of data are suitable for training different learning models.
1 code implementation • 28 Jul 2018 • Muhammad Asad, Rilwan Basaru, S M Masudur Rahman Al Arif, Greg Slabaugh
We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems.