arXiv subjects

Alin Achim

Publications and source records attributed to Alin Achim.

At least 37 records · Page 2Linked to original sources

arXiv2024

A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data

Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates visual features from lung ultrasound images with clinical data to enhance the prediction of excess body fluid. Our framework employs independent encoders to extract features for each modality and combines them through a cross-domain attention mechanism to capture complementary information. By framing the prediction as a classification task, the model achieves significantly better performance than regression. The results demonstrate that multimodal models consistently outperform single-modality models, particularly when attention mechanisms prioritize tabular data. Pseudo-sample generation further contributes to mitigating the imbalanced classification problem, achieving the highest accuracy of 88.31%. This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes.

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arXiv2024

Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction

Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts.

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arXiv2024

Deep Learning Techniques for Atmospheric Turbulence Removal: A Review

The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions.

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arXiv2024

The Quest for Early Detection of Retinal Disease: 3D CycleGAN-based Translation of Optical Coherence Tomography into Confocal Microscopy

Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, offering distinct advantages and limitations. In vivo OCT offers rapid, non-invasive imaging but can suffer from clarity issues and motion artifacts, while ex vivo confocal microscopy, providing high-resolution, cellular-detailed color images, is invasive and raises ethical concerns. To bridge the benefits of both modalities, we propose a novel framework based on unsupervised 3D CycleGAN for translating unpaired in vivo OCT to ex vivo confocal microscopy images. This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy. We also introduce a unique dataset, OCT2Confocal, comprising mouse OCT and confocal retinal images, facilitating the development of and establishing a benchmark for cross-modal image translation research. Our model has been evaluated both quantitatively and qualitatively, achieving Fréchet Inception Distance (FID) scores of 0.766 and Kernel Inception Distance (KID) scores as low as 0.153, and leading subjective Mean Opinion Scores (MOS). Our model demonstrated superior image fidelity and quality with limited data over existing methods. Our approach effectively synthesizes color information from 3D confocal images, closely approximating target outcomes and suggesting enhanced potential for diagnostic and monitoring applications in ophthalmology.

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arXiv2024

RoTIR: Rotation-Equivariant Network and Transformers for Fish Scale Image Registration

Image registration is an essential process for aligning features of interest from multiple images. With the recent development of deep learning techniques, image registration approaches have advanced to a new level. In this work, we present 'Rotation-Equivariant network and Transformers for Image Registration' (RoTIR), a deep-learning-based method for the alignment of fish scale images captured by light microscopy. This approach overcomes the challenge of arbitrary rotation and translation detection, as well as the absence of ground truth data. We employ feature-matching approaches based on Transformers and general E(2)-equivariant steerable CNNs for model creation. Besides, an artificial training dataset is employed for semi-supervised learning. Results show RoTIR successfully achieves the goal of fish scale image registration.

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arXiv2024

TaGAT: Topology-Aware Graph Attention Network For Multi-modal Retinal Image Fusion

In the realm of medical image fusion, integrating information from various modalities is crucial for improving diagnostics and treatment planning, especially in retinal health, where the important features exhibit differently in different imaging modalities. Existing deep learning-based approaches insufficiently focus on retinal image fusion, and thus fail to preserve enough anatomical structure and fine vessel details in retinal image fusion. To address this, we propose the Topology-Aware Graph Attention Network (TaGAT) for multi-modal retinal image fusion, leveraging a novel Topology-Aware Encoder (TAE) with Graph Attention Networks (GAT) to effectively enhance spatial features with retinal vasculature's graph topology across modalities. The TAE encodes the base and detail features, extracted via a Long-short Range (LSR) encoder from retinal images, into the graph extracted from the retinal vessel. Within the TAE, the GAT-based Graph Information Update (GIU) block dynamically refines and aggregates the node features to generate topology-aware graph features. The updated graph features with base and detail features are combined and decoded as a fused image. Our model outperforms state-of-the-art methods in Fluorescein Fundus Angiography (FFA) with Color Fundus (CF) and Optical Coherence Tomography (OCT) with confocal microscopy retinal image fusion. The source code can be accessed via https://github.com/xintian-99/TaGAT.

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arXiv2024

DD_RoTIR: Dual-Domain Image Registration via Image Translation and Hierarchical Feature-matching

Microscopy images obtained from multiple camera lenses or sensors in biological experiments provide a comprehensive understanding of objects from diverse perspectives. However, using multiple microscope setups increases the risk of misalignment of identical target features across different modalities, making multimodal image registration crucial. In this work, we build upon previous successes in biological image translation (XAcGAN) and mono-modal image registration (RoTIR) to develop a deep learning model, Dual-Domain RoTIR (DD_RoTIR), specifically designed to address these challenges. While GAN-based translation models are often considered inadequate for multimodal image registration, we enhance registration accuracy by employing a feature-matching algorithm based on Transformers and rotation equivariant networks. Additionally, hierarchical feature matching is utilized to tackle the complexities of multimodal image registration. Our results demonstrate that the DD_RoTIR model exhibits strong applicability and robustness across multiple microscopy image datasets.

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arXiv2024

RKFNet: A Novel Neural Network Aided Robust Kalman Filter

Driven by the filtering challenges in linear systems disturbed by non-Gaussian heavy-tailed noise, the robust Kalman filters (RKFs) leveraging diverse heavy-tailed distributions have been introduced. However, the RKFs rely on precise noise models, and large model errors can degrade their filtering performance. Also, the posterior approximation by the employed variational Bayesian (VB) method can further decrease the estimation precision. Here, we introduce an innovative RKF method, the RKFNet, which combines the heavy-tailed-distribution-based RKF framework with the deep learning (DL) technique and eliminates the need for the precise parameters of the heavy-tailed distributions. To reduce the VB approximation error, the mixing-parameter-based function and the scale matrix are estimated by the incorporated neural network structures. Also, the stable training process is achieved by our proposed unsupervised scheduled sampling (USS) method, where a loss function based on the Student's t (ST) distribution is utilised to overcome the disturbance of the noise outliers and the filtering results of the traditional RKFs are employed as reference sequences. Furthermore, the RKFNet is evaluated against various RKFs and recurrent neural networks (RNNs) under three kinds of heavy-tailed measurement noises, and the simulation results showcase its efficacy in terms of estimation accuracy and efficiency.

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arXiv2024

OCT2Confocal: 3D CycleGAN based Translation of Retinal OCT Images to Confocal Microscopy

Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, each presenting unique benefits and limitations. In-vivo OCT offers rapid, non-invasive imaging but can be hampered by clarity issues and motion artifacts. Ex-vivo confocal microscopy provides high-resolution, cellular detailed color images but is invasive and poses ethical concerns and potential tissue damage. To bridge these modalities, we developed a 3D CycleGAN framework for unsupervised translation of in-vivo OCT to ex-vivo confocal microscopy images. Applied to our OCT2Confocal dataset, this framework effectively translates between 3D medical data domains, capturing vascular, textural, and cellular details with precision. This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy. Assessed through quantitative and qualitative evaluations, the 3D CycleGAN framework demonstrates commendable image fidelity and quality, outperforming existing methods despite the constraints of limited data. This non-invasive generation of retinal confocal images has the potential to further enhance diagnostic and monitoring capabilities in ophthalmology. Our source code and OCT2Confocal dataset are available at https://github.com/xintian-99/OCT2Confocal.

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arXiv2024

On the Modelling of Ship Wakes in S-Band SAR Images and an Application to Ship Identification

We present a novel ship wake simulation system for generating S-band Synthetic Aperture Radar (SAR) images, and demonstrate the use of such imagery for the classification of ships based on their wake signatures via a deep learning approach. Ship wakes are modeled through the linear superposition of wind-induced sea elevation and the Kelvin wakes model of a moving ship. Our SAR imaging simulation takes into account frequency-dependent radar parameters, i.e., the complex dielectric constant ($\varepsilon$) and the relaxation rate ($μ$) of seawater. The former was determined through the Debye model while the latter was estimated for S-band SAR based on pre-existing values for the L, C, and X-bands. The results show good agreement between simulated and real imagery upon visual inspection. The results of implementing different training strategies are also reported, showcasing a notable improvement in accuracy of classifier achieved by integrating real and simulated SAR images during the training.

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arXiv2023

Robust Kalman Filters Based on the Sub-Gaussian $α$-stable Distribution

Motivated by filtering tasks under a linear system with non-Gaussian heavy-tailed noise, various robust Kalman filters (RKFs) based on different heavy-tailed distributions have been proposed. Although the sub-Gaussian $α$-stable (SG$α$S) distribution captures heavy tails well and is applicable in various scenarios, its potential has not yet been explored in RKFs. The main hindrance is that there is no closed-form expression of its mixing density. This paper proposes a novel RKF framework, RKF-SG$α$S, where the signal noise is assumed to be Gaussian and the heavy-tailed measurement noise is modelled by the SG$α$S distribution. The corresponding joint posterior distribution of the state vector and auxiliary random variables is approximated by the Variational Bayesian (VB) approach. Also, four different minimum mean square error (MMSE) estimators of the scale function are presented. The first two methods are based on the Importance Sampling (IS) and Gauss-Laguerre quadrature (GLQ), respectively. In contrast, the last two estimators combine a proposed Gamma series (GS) based method with the IS and GLQ estimators and hence are called GSIS and GSGL. Besides, the RKF-SG$α$S is compared with the state-of-the-art RKFs under three kinds of heavy-tailed measurement noises, and the simulation results demonstrate its estimation accuracy and efficiency. All the code needed to reproduce the results presented in this work are available at: https://github.com/PengchengH/Robust-Kalman-Filters-Based-on-the-Sub-Gaussian-alpha-stable-Distribution.

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arXiv2023

DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images

In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis. Current methods still rely on visual inspection by experts. Vision-based automatic B-line detection methods have been developed, but their performance has yet to improve in terms of both accuracy and computational speed. This paper presents a novel approach to posing B-line detection as an inverse problem via deep unfolding of the Alternating Direction Method of Multipliers (ADMM). It tackles the challenges of data labelling and model training in lung ultrasound image analysis by harnessing the capabilities of deep neural networks and model-based methods. Our objective is to substantially enhance diagnostic accuracy while ensuring efficient real-time capabilities. The results show that the proposed method runs more than 90 times faster than the traditional model-based method and achieves an F1 score that is 10.6% higher.

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arXiv2023

A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images

Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment. Manual inspection of B-lines requires experts and is time-consuming, whilst modelling automation methods is currently problematic because of a lack of ground truth. Therefore, in this paper, we propose a novel semi-supervised learning method for the B-line detection task based on contrastive learning. Through multi-level unsupervised learning on unlabelled lung ultrasound images, the features of the artefacts are learnt. In the downstream task, we introduce a fine-tuning process on a small number of labelled images using the EIoU-based loss function. Apart from reducing the data labelling workload, the proposed method shows a superior performance to model-based algorithm with the recall of 91.43%, the accuracy of 84.21% and the F1 score of 91.43%.

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arXiv2022

Current Advances in Computational Lung Ultrasound Imaging: A Review

In the field of biomedical imaging, ultrasonography has become increasingly widespread, and an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable. This article reviews the state-of-the-art in medical ultrasound image computing and in particular its application in the examination of the lungs. First, we review the current developments in medical ultrasound technology. We then focus on the characteristics of lung ultrasonography and on its ability to diagnose a variety of diseases through the identification of various artefacts. We review medical ultrasound image processing methods by splitting them into two categories: (1) traditional model-based methods, and (2) data driven methods. For the former, we consider inverse problem based methods by focusing in particular on ultrasound image despeckling, deconvolution, and line artefacts detection. Among the data-driven approaches, we discuss various works based on deep/machine learning, which include various effective network architectures implementing supervised, weakly supervised and unsupervised learning.

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arXiv2022

ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists. Therefore, an automated system is required to tackle this problem since it can relate to serious intestinal parasitic infections. This paper reviews the ICIP 2022 Challenge on parasitic egg detection and classification in microscopic images. We describe a new dataset for this application, which is the largest dataset of its kind. The methods used by participants in the challenge are summarised and discussed along with their results.

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arXiv2022

Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset

Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment. The time saved by such a process would both help ensure a prompt treatment to patients, and off-load excessive work from experts' shoulders. Advances in deep learning inspired us to exploit successful architectures for detection, adapting them to tackle a different domain. We propose a framework that exploits two such state-of-the-art models. Specifically, we demonstrate results produced by both a Generative Adversarial Network (GAN) and Faster-RCNN, for image enhancement and object detection respectively, on microscopy images of varying quality. The use of these techniques yields encouraging results, though further improvements are still needed for certain egg types whose detection still proves challenging. As a result, a new dataset has been created and made publicly available, providing an even wider range of classes and variability.

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arXiv2022

High-resolution Coastline Extraction in SAR Images via MISP-GGD Superpixel Segmentation

High accuracy coastline/shoreline extraction from SAR imagery is a crucial step in a number of maritime and coastal monitoring applications. We present a method based on image segmentation using the Generalised Gamma Mixture Model superpixel algorithm (MISP-GGD). MISP-GGD produces superpixels adhering with great accuracy to object edges in the image, such as the coastline. Unsupervised clustering of the generated superpixels according to textural and radiometric features allows for generation of a land/water mask from which a highly accurate coastline can be extracted. We present results of our proposed method on a number of SAR images of varying characteristics.

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arXiv2022

Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor

Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults. Interferometric synthetic-aperture radar (InSAR) data can be used to measure ground deformation, but its use poses distinct challenges, as the data is highly sparse and can be particularly noisy. Here we present a scheme for processing and interpolating noisy, sparse InSAR data into a dense spatio-temporal stack, helping suppress noise and opening up the possibility for treatment with deep learning and other image processing methods.

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