Search Results for author: Muhammad Asad

Found 15 papers, 8 papers with code

Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors

no code implementations25 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.

Image Segmentation Segmentation +1

Beyond the Known: Adversarial Autoencoders in Novelty Detection

no code implementations6 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.

Decoder Novelty Detection +1

Crop and Couple: cardiac image segmentation using interlinked specialist networks

1 code implementation14 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.

Anatomy Image Segmentation +2

DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces

no code implementations5 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)

Interactive Segmentation Segmentation

Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation

1 code implementation23 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.

Interactive Segmentation Segmentation

Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma

no code implementations9 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.

Management Segmentation +1

FastGeodis: Fast Generalised Geodesic Distance Transform

1 code implementation26 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.

Interactive Segmentation Medical Image Segmentation

ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

2 code implementations12 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.

Interactive Segmentation Segmentation

Federated Learning Versus Classical Machine Learning: A Convergence Comparison

no code implementations22 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.

BIG-bench Machine Learning Federated Learning +1

PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks

1 code implementation28 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.

Head Pose Estimation regression

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