pwc-archive/papers-with-abstracts huggingface dataset:
Mixture of Multicenter Experts in Multimodal Generative AI for Advanced Radiotherapy Target Delineation
Authors: Yujin Oh, Sangjoon Park, Xiang Li, Wang Yi, Jonathan Paly, Jason Efstathiou, Annie Chan, Jun Won Kim, Hwa Kyung Byun, Ik Jae Lee, Jaeho Cho, Chan Woo Wee, Peng Shu, Peilong Wang, Nathan Yu, Jason Holmes, Jong Chul Ye, Quanzheng Li, Wei Liu, Woong Sub Koom, Jin Sung Kim, Kyungsang Kim
Date: 2024-09-27 00:00:00

Clinical experts employ diverse philosophies and strategies in patient care, influenced by regional patient populations. However, existing medical artificial intelligence (AI) models are often trained on data distributions that disproportionately reflect highly prevalent patterns, reinforcing biases and overlooking the diverse expertise of clinicians. To overcome this limitation, we introduce the Mixture of Multicenter Experts (MoME) approach. This method strategically integrates specialized expertise from diverse clinical strategies, enhancing the AI model's ability to generalize and adapt across multiple medical centers. The MoME-based multimodal target volume delineation model, trained with few-shot samples including images and clinical notes from each medical center, outperformed baseline methods in prostate cancer radiotherapy target delineation. The advantages of MoME were most pronounced when data characteristics varied across centers or when data availability was limited, demonstrating its potential for broader clinical applications. Therefore, the MoME framework enables the deployment of AI-based target volume delineation models in resource-constrained medical facilities by adapting to specific preferences of each medical center only using a few sample data, without the need for data sharing between institutions. Expanding the number of multicenter experts within the MoME framework will significantly enhance the generalizability, while also improving the usability and adaptability of clinical AI applications in the field of precision radiation oncology.
PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models
Authors: Kar Balan, Andrew Gilbert, John Collomosse
Date: 2024-09-26 00:00:00
Tasks: Federated Learning, Memorization, Privacy Preserving

We present PDFed, a decentralized, aggregator-free, and asynchronous federated learning protocol for training image diffusion models using a public blockchain. In general, diffusion models are prone to memorization of training data, raising privacy and ethical concerns (e.g., regurgitation of private training data in generated images). Federated learning (FL) offers a partial solution via collaborative model training across distributed nodes that safeguard local data privacy. PDFed proposes a novel sample-based score that measures the novelty and quality of generated samples, incorporating these into a blockchain-based federated learning protocol that we show reduces private data memorization in the collaboratively trained model. In addition, PDFed enables asynchronous collaboration among participants with varying hardware capabilities, facilitating broader participation. The protocol records the provenance of AI models, improving transparency and auditability, while also considering automated incentive and reward mechanisms for participants. PDFed aims to empower artists and creators by protecting the privacy of creative works and enabling decentralized, peer-to-peer collaboration. The protocol positively impacts the creative economy by opening up novel revenue streams and fostering innovative ways for artists to benefit from their contributions to the AI space.
A novel brain registration model combining structural and functional MRI information
Authors: Baolong Li, Yuhu Shi, Lei Wang, Weiming Zeng, Changming Zhu
Date: 2024-09-26 00:00:00
Tasks: Functional Connectivity

Although developed functional magnetic resonance imaging (fMRI) registration algorithms based on deep learning have achieved a certain degree of alignment of functional area, they underutilized fine structural information. In this paper, we propose a semi-supervised convolutional neural network (CNN) registration model that integrates both structural and functional MRI information. The model first learns to generate deformation fields by inputting structural MRI (T1w-MRI) into the CNN to capture fine structural information. Then, we construct a local functional connectivity pattern to describe the local fMRI information, and use the Bhattacharyya coefficient to measure the similarity between two fMRI images, which is used as a loss function to facilitate the alignment of functional areas. In the inter-subject registration experiment, our model achieved an average number of voxels exceeding the threshold of 4.24 is 2248 in the group-level t-test maps for the four functional brain networks (default mode network, visual network, central executive network, and sensorimotor network). Additionally, the atlas-based registration experiment results show that the average number of voxels exceeding this threshold is 3620. The results are the largest among all methods. Our model achieves an excellent registration performance in fMRI and improves the consistency of functional regions. The proposed model has the potential to optimize fMRI image processing and analysis, facilitating the development of fMRI applications.
Morphological-consistent Diffusion Network for Ultrasound Coronal Image Enhancement
Authors: Yihao Zhou, Zixun Huang, Timothy Tin-Yan Lee, Chonglin Wu, Kelly Ka-Lee Lai, De Yang, Alec Lik-hang Hung, Jack Chun-Yiu Cheng, Tsz-Ping Lam, Yong-Ping Zheng
Date: 2024-09-25 00:00:00
Tasks: Image Enhancement, Image Generation

Ultrasound curve angle (UCA) measurement provides a radiation-free and reliable evaluation for scoliosis based on ultrasound imaging. However, degraded image quality, especially in difficult-to-image patients, can prevent clinical experts from making confident measurements, even leading to misdiagnosis. In this paper, we propose a multi-stage image enhancement framework that models high-quality image distribution via a diffusion-based model. Specifically, we integrate the underlying morphological information from images taken at different depths of the 3D volume to calibrate the reverse process toward high-quality and high-fidelity image generation. This is achieved through a fusion operation with a learnable tuner module that learns the multi-to-one mapping from multi-depth to high-quality images. Moreover, the separate learning of the high-quality image distribution and the spinal features guarantees the preservation of consistent spinal pose descriptions in the generated images, which is crucial in evaluating spinal deformities. Remarkably, our proposed enhancement algorithm significantly outperforms other enhancement-based methods on ultrasound images in terms of image quality. Ultimately, we conduct the intra-rater and inter-rater measurements of UCA and higher ICC (0.91 and 0.89 for thoracic and lumbar angles) on enhanced images, indicating our method facilitates the measurement of ultrasound curve angles and offers promising prospects for automated scoliosis diagnosis.
Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data
Authors: Ligen Shi, Xu Jiang, Yunze Liu, Chang Liu, Ping Yang, Shifeng Guo, Xing Zhao
Date: 2024-09-24 00:00:00

Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading image quality. This paper proposes a method for correcting stripe artifacts in the sinogram data. The proposed method leverages implicit neural representation (INR) to correct defective pixel response values using implicit continuous functions and simultaneously learns stripe features in the angular direction of the sinogram data. These two components are combined within an optimization constraint framework, achieving unsupervised iterative correction of stripe artifacts in the projection domain. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in removing ring artifacts while maintaining the clarity of CT images.
χ-sepnet: Deep neural network for magnetic susceptibility source separation
Authors: Minjun Kim, Sooyeon Ji, Jiye Kim, Kyeongseon Min, Hwihun Jeong, Jonghyo Youn, Taechang Kim, Jinhee Jang, Berkin Bilgic, Hyeong-Geol Shin, Jongho Lee
Date: 2024-09-21 00:00:00

Magnetic susceptibility source separation ($\chi$-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of para- and diamagnetic susceptibility source distributions in the brain. The method utilizes reversible transverse relaxation (R2'=R2*-R2) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for R2 in addition R2*. To address this challenge, we develop a new deep learning network, $\chi$-sepnet, and propose two deep learning-based susceptibility source separation pipelines, $\chi$-sepnet-R2' for inputs with multi-echo GRE and multi-echo spin-echo, and $\chi$-sepnet-R2* for input with multi-echo GRE only. $\chi$-sepnet is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality $\chi$-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from conventional regularization-based reconstruction methods. In quantitative analysis, $\chi$-sepnet-R2' achieves the best outcomes followed by $\chi$-sepnet-R2*, outperforming the conventional methods. When the lesions of multiple sclerosis patients are assessed, both pipelines report identical lesion characteristics in most lesions ($\chi$para: 99.6% and $\chi$dia: 98.4% out of 250 lesions). The $\chi$-sepnet-R2* pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary.
Deep Learning-Based Automated Post-Operative Gross Tumor Volume Segmentation in Glioblastoma Patients
Authors: Rajarajeswari Muthusivarajan, Adrian Celaya, Maguy Farhat, Wasif Talpur, Holly Langshaw, Victoria White, Andrew Elliott, Sara Thrower, Dawid Schellingerhout, David Fuentes, Caroline Chung
Date: 2024-09-23 00:00:00

Precise automated delineation of post-operative gross tumor volume in glioblastoma cases is challenging and time-consuming owing to the presence of edema and the deformed brain tissue resulting from the surgical tumor resection. To develop a model for automated delineation of post-operative gross tumor volumes in glioblastoma, we proposed a novel 3D double pocket U-Net architecture that has two parallel pocket U-Nets. Both U-Nets were trained simultaneously with two different subsets of MRI sequences and the output from the models was combined to do the final prediction. We strategically combined the MRI input sequences (T1, T2, T1C, FL) for model training to achieve improved segmentation accuracy. The dataset comprised 82 post-operative studies collected from 23 glioblastoma patients who underwent maximal safe tumor resection. All had gross tumor volume (GTV) segmentations performed by human experts, and these were used as a reference standard. The results of 3D double pocket U-Net were compared with baseline 3D pocket U-Net models and the ensemble of 3D pocket U-Net models. All the models were evaluated with fivefold cross-validation in terms of the Dice similarity coefficient and Hausdorff distance. Our proposed double U-Net model trained with input sequences [T1, T1C, FL + T2, T1C] achieved a better mean Dice score of 0.8585 and Hausdorff distance of 4.1942 compared to all the baseline models and ensemble models trained. The presence of infiltrating tumors and vasogenic edema in the post-operative MRI scans tends to reduce segmentation accuracy when considering the MRI sequences T1, T2, T1C, and FL together for model training. The double U-Net approach of combining subsets of the MRI sequences as distinct inputs for model training improves segmentation accuracy by 7% when compared with the conventional method of model training with all four sequences.
Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections
Authors: Olivier Leblanc, Yves Wiaux, Laurent Jacques
Date: 2024-09-23 00:00:00
Tasks: Compressive Sensing, Image Reconstruction, Radio Interferometry

Radio-interferometry (RI) observes the sky at unprecedented angular resolutions, enabling the study of several far-away galactic objects such as galaxies and black holes. In RI, an array of antennas probes cosmic signals coming from the observed region of the sky. The covariance matrix of the vector gathering all these antenna measurements offers, by leveraging the Van Cittert-Zernike theorem, an incomplete and noisy Fourier sensing of the image of interest. The number of noisy Fourier measurements -- or visibilities -- scales as $\mathcal O(Q^2B)$ for $Q$ antennas and $B$ short-time integration (STI) intervals. We address the challenges posed by this vast volume of data, which is anticipated to increase significantly with the advent of large antenna arrays, by proposing a compressive sensing technique applied directly at the level of the antenna measurements. First, this paper shows that beamforming -- a common technique of dephasing antenna signals -- usually used to focus some region of the sky, is equivalent to sensing a rank-one projection (ROP) of the signal covariance matrix. We build upon our recent work arXiv:2306.12698v3 [eess.IV] to propose a compressive sensing scheme relying on random beamforming, trading the $Q^2$-dependence of the data size for a smaller number $N_{\mathrm p}$ ROPs. We provide image recovery guarantees for sparse image reconstruction. Secondly, the data size is made independent of $B$ by applying $N_{\mathrm m}$ Bernoulli modulations of the ROP vectors obtained for the STI. The resulting sample complexities, theoretically derived in a simpler case without modulations and numerically obtained in phase transition diagrams, are shown to scale as $\mathcal O(K)$ where $K$ is the image sparsity. This illustrates the potential of the approach.
Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model
Authors: Anurag Malyala, Zhenlin Zhang, Chengyan Wang, Chen Qin
Date: 2024-09-22 00:00:00
Tasks: Diagnostic, MRI Reconstruction

Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times. Whilst present deep learning-based approaches have demonstrated potential in expediting MRI processes, these methods usually rely on known sampling patterns and exhibit limited generalisability to novel patterns. In the paper, we propose a sampling-pattern-agnostic MRI reconstruction method via a diffusion model through adaptive consistency enforcement. Our approach effectively reconstructs high-fidelity images with varied under-sampled acquisitions, generalising across contrasts and acceleration factors regardless of sampling trajectories. We train and validate across all contrasts in the MICCAI 2024 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset for the ``Random sampling CMR reconstruction'' task. Evaluation results indicate that our proposed method significantly outperforms baseline methods.
Real-time Detection and Auto focusing of Beam Profiles from Silicon Photonics Gratings using YOLO model
Authors: Yu Dian Lim, Hong Yu Li, Simon Chun Kiat Goh, Xiangyu Wang, Peng Zhao, Chuan Seng Tan
Date: 2024-09-22 00:00:00

When observing the chip-to-free-space light beams from silicon photonics (SiPh) to free-space, manual adjustment of camera lens is often required to obtain a focused image of the light beams. In this letter, we demonstrated an auto-focusing system based on you-only-look-once (YOLO) model. The trained YOLO model exhibits high classification accuracy of 99.7% and high confidence level >0.95 when detecting light beams from SiPh gratings. A video demonstration of real-time light beam detection, real-time computation of beam width, and auto focusing of light beams are also included.
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement
Authors: Cameron Khanpour
Date: 2024-09-22 00:00:00
Tasks: Denoising, Image Denoising, Image Enhancement, Image Quality Assessment, SSIM

This paper presents a comprehensive analysis of image denoising techniques, primarily focusing on Non-local Means (NLM) and Daubechies Soft Wavelet Thresholding, and their efficacy across various datasets. These methods are applied to the CURE-OR, CURE-TSD, CURE-TSR, SSID, and Set-12 datasets, followed by an evaluation using Image Quality Assessment (IQA) metrics PSNR, SSIM, CW-SSIM, UNIQUE, MS-UNIQUE, CSV, and SUMMER. The results indicate that NLM and Wavelet Thresholding perform optimally on Set12 and SIDD datasets, attributed to their ability to effectively handle general additive and multiplicative noise masks. However, their performance on CURE datasets is limited due to the presence of complex distortions like Dirty Lens and Codec Error, which these methods are not well-suited to address. Analysis between NLM and Wavelet Thresholding shows that while NLM generally offers superior visual quality, Wavelet Thresholding excels in specific IQA metrics, particularly SUMMER, due to its enhancement in the frequency domain as opposed to NLM's spatial domain approach.
Unsupervised Learning of Multi-modal Affine Registration for PET/CT
Authors: Junyu Chen, Yihao Liu, Shuwen Wei, Aaron Carass, Yong Du
Date: 2024-09-20 00:00:00
Tasks: Image Registration

Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Despite the significant promise shown by recent deep learning (DL)-based methods in various medical imaging applications, their application to multi-modal PET/CT affine registration remains relatively unexplored. This study investigates a DL-based approach for PET/CT affine registration. We introduce a novel method using Parzen windowing to approximate the correlation ratio, which acts as the image similarity measure for training DNNs in multi-modal registration. Additionally, we propose a multi-scale, instance-specific optimization scheme that iteratively refines the DNN-generated affine parameters across multiple image resolutions. Our method was evaluated against the widely used mutual information metric and a popular optimization-based technique from the ANTs package, using a large public FDG-PET/CT dataset with synthetic affine transformations. Our approach achieved a mean Dice Similarity Coefficient (DSC) of 0.870, outperforming the compared methods and demonstrating its effectiveness in multi-modal PET/CT image registration.
Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction
Authors: Odysseas Pappas, Jonathan Mamou, Adrian Basarab, Denis Kouame, Alin Achim
Date: 2024-09-20 00:00:00
Tasks: 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.
PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution
Authors: Yan Wang, Yusen Li, Gang Wang, Xiaoguang Liu
Date: 2024-09-20 00:00:00
Tasks: Super-Resolution

Reducing latency is a roaring trend in recent super-resolution (SR) research. While recent progress exploits various convolutional blocks, attention modules, and backbones to unlock the full potentials of the convolutional neural network (ConvNet), achieving real-time performance remains a challenge. To this end, we present PlainUSR, a novel framework incorporating three pertinent modifications to expedite ConvNet for efficient SR. For the convolutional block, we squeeze the lighter but slower MobileNetv3 block into a heavier but faster vanilla convolution by reparameterization tricks to balance memory access and calculations. For the attention module, by modulating input with a regional importance map and gate, we introduce local importance-based attention to realize high-order information interaction within a 1-order attention latency. As to the backbone, we propose a plain U-Net that executes channel-wise discriminate splitting and concatenation. In the experimental phase, PlainUSR exhibits impressively low latency, great scalability, and competitive performance compared to both state-of-the-art latency-oriented and quality-oriented methods. In particular, compared to recent NGswin, the PlainUSR-L is 16.4x faster with competitive performance.
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation
Authors: Markus Unterdechler, Botond Fazekas, Guilherme Aresta, Hrvoje Bogunović
Date: 2024-09-20 00:00:00
Tasks: Data Augmentation

Data augmentation plays a crucial role in addressing the challenge of limited expert-annotated datasets in deep learning applications for retinal Optical Coherence Tomography (OCT) scans. This work exhaustively investigates the impact of various data augmentation techniques on retinal layer boundary and fluid segmentation. Our results reveal that their effectiveness significantly varies based on the dataset's characteristics and the amount of available labeled data. While the benefits of augmentation are not uniform - being more pronounced in scenarios with scarce data, particularly for transformation-based methods - the findings highlight the necessity of a strategic approach to data augmentation. It is essential to note that the effectiveness of data augmentation varies significantly depending on the characteristics of the dataset. The findings emphasize the need for a nuanced approach, considering factors like dataset characteristics, the amount of labelled data, and the choice of model architecture.