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Researcher profile

Shujaat Khan

Shujaat Khan contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningComputer Visioneess.IVmath.OCeess.SPeess.SYSystems and ControlApplicationsArtificial IntelligenceBiomoleculesInformation Theorymath.ITMethodologyQuantitative Methods

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Published work

12 published item(s)

preprint2026arXiv

Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection

Face presentation attack detection (FacePAD) remains challenging under diverse spoofing representation, including 2D print and replay, 3D mask-based spoofing, makeup-induced appearance manipulation, and physical occlusions, as well as under varying capture conditions. Motion cues are highly discriminative for FacePAD but typically require explicit optical flow estimation, which introduces substantial computational overhead and limits real-time deployment. In this work, we leverage optical flow to enhance motion representation during training while eliminating the need for flow computation at inference. We propose a dual-branch teacher model that fuses appearance cues from RGB frames with motion cues derived from colorwheel-encoded optical flow, enabling effective modeling of micro-motions and temporal consistency. To enable efficient deployment, we introduce a knowledge distillation framework that transfers motion-aware knowledge from the flow-augmented teacher to a lightweight RGB-only student via logit distillation. As a result, the student implicitly learns motion-sensitive representations without requiring explicit flow estimation or additional feature extraction blocks at inference. Extensive experiments demonstrate strong performance across multiple benchmarks, achieving 0.0% HTER on Replay-Attack and Replay-Mobile, 0.94% HTER on ROSE-Youtu, 5.65% HTER on SiW-Mv2, and 0.42% ACER on OULU-NPU. The distilled student achieves performance comparable to or better than the teacher while significantly reducing parameters and FLOPs, achieving 52 FPS on an NVIDIA Jetson Orin Nano, indicating its suitability for real-time and resource-constrained FacePAD deployment.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning

Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applications due to the phase aberration artefact caused by variations in the speed of sound (SoS) within body parts. To address this, here we propose a novel self-supervised 3D CNN that enables phase aberration robust plane-wave imaging. Instead of aiming at estimating the SoS distribution as in conventional methods, our approach is unique in that the network is trained in a self-supervised manner to robustly generate a high-quality image from various phase aberrated images by modeling the variation in the speed of sound as stochastic. Experimental results using real measurements from tissue-mimicking phantom and \textit{in vivo} scans confirmed that the proposed method can significantly reduce the phase aberration artifacts and improve the visual quality of deep scans.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here we propose a deep learning-based beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or sub-sampled radio-frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high quality ultrasound images using a single beamformer. The origin of such input-dependent adaptivity is also theoretically analyzed. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comments on "A new computing approach for power signal modeling using fractional adaptive algorithms"

In this paper, some points to the convergence analysis performed in the paper [A new computing approach for power signal modeling using fractional adaptive algorithms, ISA Transactions 68 (2017) 189-202] are presented. It is highlighted that the way the authors prove convergence, suffers lack of correct and valid mathematical justifications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"

The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in ``Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization'' [Nonlinear Dyn. 88(2), 839-858 (2017)]. It is observed that these algorithms do not always converge whereas they have apparently no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning and are supported by numerical simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comments on "Generalization of the gradient method with fractional order gradient direction"

In this paper, a detrimental mathematical mistake is pointed out in the proof of \textit{Theorem 1} presented in the paper\textit{ [Generalization of the gradient method with fractional order gradient direction, J. Franklin Inst., 357 (2020) 2514-2532]}. It is highlighted that the way the authors prove the convergence of the fractional extreme points of a real valued function to its integer order extreme points lacks correct and valid mathematical argument. Rest of the theorems contained in the paper are mostly announced without any proof relaying on that of Theorem 1.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

E3-targetPred: Prediction of E3-Target Proteins Using Deep Latent Space Encoding

Understanding E3 ligase and target substrate interactions are important for cell biology and therapeutic development. However, experimental identification of E3 target relationships is not an easy task due to the labor-intensive nature of the experiments. In this article, a sequence-based E3-target prediction model is proposed for the first time. The proposed framework utilizes composition of k-spaced amino acid pairs (CKSAAP) to learn the relationship between E3 ligases and their target protein. A class separable latent space encoding scheme is also devised that provides a compressed representation of feature space. A thorough ablation study is performed to identify an optimal gap size for CKSAAP and the number of latent variables that can represent the E3-target relationship successfully. The proposed scheme is evaluated on an independent dataset for a variety of standard quantitative measures. In particular, it achieves an average accuracy of $70.63\%$ on an independent dataset. The source code and datasets used in the study are available at the author's GitHub page (https://github.com/psychemistz/E3targetPred).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

GSSMD: New metric for robust and interpretable assay quality assessment and hit selection

In the high-throughput screening (HTS) campaigns, the Z'-factor and strictly standardized mean difference (SSMD) are commonly used to assess the quality of assays and to select hits. However, these measures are vulnerable to outliers and their performances are highly sensitive to background distributions. Here, we propose an alternative measure for assay quality assessment and hit selection. The proposed method is a non-parametric generalized variant of SSMD (GSSMD). In this paper, we have shown that the proposed method provides more robust and intuitive way of assay quality assessment and hit selection.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-Kernel Fusion for RBF Neural Networks

A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN

Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. Classic approaches for solving these problems are usually model-based iterative approaches that have been developed specifically for each type of artifact, which are often computationally intensive. Recently, deep learning approaches have been proposed as computationally efficient and high performance alternatives. Unfortunately, in the current deep learning approaches, a dedicated neural network should be trained with matched training data for each specific artifact type. This poses a fundamental limitation in the practical use of deep learning for US, since large number of models should be stored to deal with various US image artifacts. Inspired by the recent success of multi-domain image transfer, here we propose a novel, unsupervised, deep learning approach in which a single neural network can be used to deal with different types of US artifacts simply by changing a mask vector that switches between different target domains. Our algorithm is rigorously derived using an optimal transport (OT) theory for cascaded probability measures. Experimental results using phantom and in vivo data demonstrate that the proposed method can generate high quality image by removing distinct artifacts, which are comparable to those obtained by separately trained multiple neural networks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal

Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfortunately, it often suffers from poor visual quality from various origins, such as speckle noises, blurring, multi-line acquisition (MLA), limited RF channels, small number of view angles for the case of plane wave imaging, etc. Classical methods to deal with these problems include image-domain signal processing approaches using various adaptive filtering and model-based approaches. Recently, deep learning approaches have been successfully used for ultrasound imaging field. However, one of the limitations of these approaches is that paired high quality images for supervised training are difficult to obtain in many practical applications. In this paper, inspired by the recent theory of unsupervised learning using optimal transport driven cycleGAN (OT-cycleGAN), we investigate applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Experimental results for various tasks such as deconvolution, speckle removal, limited data artifact removal, etc. confirmed that our unsupervised learning method provides comparable results to supervised learning for many practical applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Switchable Deep Beamformer

Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be combined with the beamforming. Unfortunately, in the current technology, a separate beamformer should be trained and stored for each application, demanding significant scanner resources. To address this problem, here we propose a {\em switchable} deep beamformer that can produce various types of output such as DAS, speckle removal, deconvolution, etc., using a single network with a simple switch. In particular, the switch is implemented through Adaptive Instance Normalization (AdaIN) layers, so that various output can be generated by merely changing the AdaIN code. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed methods for various applications.

0 citations0 reviewsNo code linkedOpen access