pwc-archive/papers-with-abstracts huggingface dataset:
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
Authors: Priscille de Dumast, Thomas Sanchez, Hélène Lajous, Meritxell Bach Cuadra
Date: 2022-11-25 00:00:00
Tasks: Super-Resolution

Tuning the regularization hyperparameter $\alpha$ in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of $\alpha$ to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune $\alpha$ for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal $\alpha$, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting.
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
Authors: Ricardo Augusto Borsoi, Isabell Lehmann, Mohammad Abu Baker Siddique Akhonda, Vince Calhoun, Konstantin Usevich, David Brie, Tülay Adalı
Date: 2022-11-25 00:00:00
Tasks: Tensor Decomposition

Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data. Coupled matrix and tensor factorization approaches have been useful for flexible data fusion, or decomposition to extract features that can be used in multiple ways. However, existing methods do not directly recover shared and dataset-specific components, which requires post-processing steps involving additional hyperparameter selection. In this paper, we propose a tensor-based framework for multi-task fMRI data fusion, using a partially constrained canonical polyadic (CP) decomposition model. Differently from previous approaches, the proposed method directly recovers shared and dataset-specific components, leading to results that are directly interpretable. A strategy to select a highly reproducible solution to the decomposition is also proposed. We evaluate the proposed methodology on real fMRI data of three tasks, and show that the proposed method finds meaningful components that clearly identify group differences between patients with schizophrenia and healthy controls.
FPGA Implementation of An Event-driven Saliency-based Selective Attention Model
Authors: Hajar Asgari, Nicoletta Risi, Giacomo Indiveri
Date: 2022-11-25 00:00:00
Tasks: Event-based vision

Artificial vision systems of autonomous agents face very difficult challenges, as their vision sensors are required to transmit vast amounts of information to the processing stages, and to process it in real-time. One first approach to reduce data transmission is to use event-based vision sensors, whose pixels produce events only when there are changes in the input. However, even for event-based vision, transmission and processing of visual data can be quite onerous. Currently, these challenges are solved by using high-speed communication links and powerful machine vision processing hardware. But if resources are limited, instead of processing all the sensory information in parallel, an effective strategy is to divide the visual field into several small sub-regions, choose the region of highest saliency, process it, and shift serially the focus of attention to regions of decreasing saliency. This strategy, commonly used also by the visual system of many animals, is typically referred to as ``selective attention''. Here we present a digital architecture implementing a saliency-based selective visual attention model for processing asynchronous event-based sensory information received from a DVS. For ease of prototyping, we use a standard digital design flow and map the architecture on an FPGA. We describe the architecture block diagram highlighting the efficient use of the available hardware resources demonstrated through experimental results exploiting a hardware setup where the FPGA interfaced with the DVS camera.
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images
Authors: Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş, Marco Allinovi, Alin Achim
Date: 2022-11-25 00:00:00
Tasks: Contrastive Learning, Line Detection

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%.
A mathematical theory of resolution limits for super-resolution of positive sources
Authors: Ping Liu, Yanchen He, Habib Ammari
Date: 2022-11-24 00:00:00
Tasks: Super-Resolution

A priori information on the positivity of source intensities is ubiquitous in imaging fields and is also important for a multitude of super-resolution and deconvolution algorithms. However, the fundamental resolution limit of positive sources is still unknown, and research in this field is very limited indeed. In this work, we analyze the super-resolving capacity for number and location recoveries in the super-resolution of positive sources and aim to answer the resolution limit problem in a rigorous manner. Specifically, we introduce the computational resolution limit for respectively the number detection and location recovery in the one-dimensional super-resolution problem and quantitatively characterize their dependency on the cutoff frequency, signal-to-noise ratio, and the sparsity of the sources. As a direct consequence, we show that targeting at the sparest positive solution in the super-resolution already provides the optimal resolution order. These results are generalized to multi-dimensional spaces. Our estimates indicate that there exist phase transitions in the corresponding reconstructions, which are confirmed by numerical experiments. On the other hand, despite the fact that positivity plays important roles in improving the resolution of certain super-resolution algorithms, our theory has made several different but significant discoveries: i) The a priori information of positivity cannot further improve the order of the resolution limit; ii) The positivity of the source sometimes deteriorates the resolution limit instead of enhancing it. In particular, under certain signal-to-noise ratio, two point sources with different phases actually have a better resolution limit than those with the same one.
On the impact of incorporating task-information in learning-based image denoising
Authors: Kaiyan Li, Hua Li, Mark A. Anastasio
Date: 2022-11-23 00:00:00
Tasks: Computed Tomography (CT), Denoising, Image Denoising, SSIM, Transfer Learning

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss functions that quantify a distance between the denoised image, or a transformed version of it, and the defined target image (e.g., a noise-free or low-noise image). They have demonstrated high performance in terms of traditional image quality metrics such as root mean square error (RMSE), structural similarity index measure (SSIM), or peak signal-to-noise ratio (PSNR). However, it has been reported recently that such denoising methods may not always improve objective measures of image quality. In this work, a task-informed DNN-based image denoising method was established and systematically evaluated. A transfer learning approach was employed, in which the DNN is first pre-trained by use of a conventional (non-task-informed) loss function and subsequently fine-tuned by use of the hybrid loss that includes a task-component. The task-component was designed to measure the performance of a numerical observer (NO) on a signal detection task. The impact of network depth and constraining the fine-tuning to specific layers of the DNN was explored. The task-informed training method was investigated in a stylized low-dose X-ray computed tomography (CT) denoising study for which binary signal detection tasks under signal-known-statistically (SKS) with background-known-statistically (BKS) conditions were considered. The impact of changing the specified task at inference time to be different from that employed for model training, a phenomenon we refer to as "task-shift", was also investigated. The presented results indicate that the task-informed training method can improve observer performance while providing control over the trade off between traditional and task-based measures of image quality.
Generalization of Artificial Intelligence Models in Medical Imaging: A Case-Based Review
Authors: Rishi Gadepally, Andrew Gomella, Eric Gingold, Paras Lakhani
Date: 2022-11-15 00:00:00

The discussions around Artificial Intelligence (AI) and medical imaging are centered around the success of deep learning algorithms. As new algorithms enter the market, it is important for practicing radiologists to understand the pitfalls of various AI algorithms. This entails having a basic understanding of how algorithms are developed, the kind of data they are trained on, and the settings in which they will be deployed. As with all new technologies, use of AI should be preceded by a fundamental understanding of the risks and benefits to those it is intended to help. This case-based review is intended to point out specific factors practicing radiologists who intend to use AI should consider.
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
Authors: Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao
Date: 2022-11-12 00:00:00
Tasks: COVID-19 Diagnosis, Decoder, Image Captioning, Medical Report Generation

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular, given a medical image, DeltaNet employs three steps to generate a report: 1) first retrieving related medical reports, i.e., the historical reports from the same or similar patients; 2) then comparing retrieved images and current image to find the differences; 3) finally generating a new report to accommodate identified differences based on the conditional report. We evaluate DeltaNet on a COVID-19 dataset, where DeltaNet outperforms state-of-the-art approaches. Besides COVID-19, the proposed DeltaNet can be applied to other diseases as well. We validate its generalization capabilities on the public IU-Xray and MIMIC-CXR datasets for chest-related diseases. Code is available at \url{https://github.com/LX-doctorAI1/DeltaNet}.
Secure Power Control for Downlink Cell-Free Massive MIMO With Passive Eavesdroppers
Authors: Junguk Park, Sangseok Yun, Jeongseok Ha
Date: 2022-11-25 00:00:00
Tasks: Diversity

This work studies secure communications for a cell-free massive multiple-input multiple-output (CF-mMIMO) network which is attacked by multiple passive eavesdroppers overhearing communications between access points (APs) and users in the network. It will be revealed that the distributed APs in CF-mMIMO allows not only legitimate users but also eavesdroppers to reap the diversity gain, which seriously degrades secrecy performance. Motivated by this, this work proposes an artificial noise (AN)-aided secure power control scheme for CF-mMIMO under passive eavesdropping aiming to achieve a higher secrecy rate and/or guarantee security. In particular, it will be demonstrated that a careful use of AN signal in the power control is especially important to improve the secrecy performance. The performance of the proposed power control scheme is evaluated and compared with various power control schemes via numerical experiments, which clearly shows that the proposed power control scheme outperforms all the competing schemes.
On the Impact of Phase Noise on Monostatic Sensing in OFDM ISAC Systems
Authors: Musa Furkan Keskin, Carina Marcus, Olof Eriksson, Henk Wymeersch, Visa Koivunen
Date: 2022-11-24 00:00:00
Tasks: ISAC

Phase noise (PN) can become a major bottleneck for integrated sensing and communications (ISAC) systems towards 6G wireless networks. In this paper, we consider an OFDM ISAC system with oscillator imperfections and investigate the impact of PN on monostatic sensing performance by performing a misspecified Cram\'er-Rao bound (MCRB) analysis. Simulations are carried out under a wide variety of operating conditions with regard to SNR, oscillator type (free-running oscillators (FROs) and phase-locked loops (PLLs)), 3-dB bandwidth of the oscillator spectrum, PLL loop bandwidth and target range. The results provide valuable insights on when PN leads to a significant degradation in range and/or velocity accuracy, establishing important guidelines for hardware and algorithm design in 6G ISAC systems.
Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems
Authors: Yasaman Abdollahian, Ehsan Tohidi, Martin Kasparick, Li Wang, Ahmet Hasim Gokceoglu, Slawomir Stanczak
Date: 2022-11-24 00:00:00

MIMO technology has enabled spatial multiple access and has provided a higher system spectral efficiency (SE). However, this technology has some drawbacks, such as the high number of RF chains that increases complexity in the system. One of the solutions to this problem can be to employ reconfigurable antennas (RAs) that can support different radiation patterns during transmission to provide similar performance with fewer RF chains. In this regard, the system aims to maximize the SE with respect to optimum beamforming design and RA mode selection. Due to the non-convexity of this problem, we propose machine learning-based methods for RA antenna mode selection in both dynamic and static scenarios. In the static scenario, we present how to solve the RA mode selection problem, an integer optimization problem in nature, via deep convolutional neural networks (DCNN). A Multi-Armed-bandit (MAB) consisting of offline and online training is employed for the dynamic RA state selection. For the proposed MAB, the computational complexity of the optimization problem is reduced. Finally, the proposed methods in both dynamic and static scenarios are compared with exhaustive search and random selection methods.
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Authors: Yihui Huang, Zi Wang, Xinlin Zhang, Jian Cao, Zhangren Tu, Meijin Lin, Di Guo, Xiaobo Qu
Date: 2022-11-24 00:00:00
Tasks: Deep Learning, Rolling Shutter Correction

Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimposed exponential functions, deep learning has achieved fast and high-fidelity signal reconstruction by training a mapping from the undersampled exponentials to the fully sampled ones. However, the mismatch, such as the sampling rate of undersampling, the organ and the contrast of imaging, between the training and target data will heavily compromise the reconstruction. To address this issue, we propose Alternating Deep Low-Rank (ADLR), which combines deep learning solvers and classic optimization solvers. Experiments on the reconstruction of synthetic and realistic biomedical magnetic resonance signals demonstrate that ADLR can effectively mitigate the mismatch issue and achieve lower reconstruction errors than state-of-the-art methods.
Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems
Authors: Govind R. Gopal, Bhaskar D. Rao
Date: 2022-11-23 00:00:00
Tasks: Quantization

We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput. In this regard, we formulate two main placement problems, namely the sum rate and minimum rate maximization problems, and discuss the challenges associated with solving the underlying optimization problem with the help of some simple scenarios. As a practical solution to the AP placement problem, we suggest a vector quantization (VQ) approach. The suitability of the VQ approach to cell-free AP placement is investigated by examining three VQ-based solutions. First, the standard VQ approach, that is the Lloyd algorithm (using the squared error distortion function) is described. Second, the tree-structured VQ (TSVQ), which performs successive partitioning of the distribution space is applied. Third, a probability density function optimized VQ (PDFVQ) procedure is outlined, which enables efficient, low complexity, and scalable placement, and is aimed at a massive distributed multiple-input-multiple-output (MIMO) scenario. While the VQ-based solutions do not solve the cell-free AP placement problems explicitly, numerical experiments show that their sum and minimum rate performances are good enough, and offer a good starting point for gradient-based optimization methods. Among the VQ solutions, PDFVQ, with advantages over the other VQ methods, offers a good trade-off between sum and minimum rates.
Meta-Learning the Inductive Biases of Simple Neural Circuits
Authors: William Dorrell, Maria Yuffa, Peter Latham
Date: 2022-11-24 00:00:00
Tasks: Inductive Bias, Meta-Learning

Training data is always finite, making it unclear how to generalise to unseen situations. But, animals do generalise, wielding Occam's razor to select a parsimonious explanation of their observations. How they do this is called their inductive bias, and it is implicitly built into the operation of animals' neural circuits. This relationship between an observed circuit and its inductive bias is a useful explanatory window for neuroscience, allowing design choices to be understood normatively. However, it is generally very difficult to map circuit structure to inductive bias. Here, we present a neural network tool to bridge this gap. The tool meta-learns the inductive bias by learning functions that a neural circuit finds easy to generalise, since easy-to-generalise functions are exactly those the circuit chooses to explain incomplete data. In systems with analytically known inductive bias, i.e. linear and kernel regression, our tool recovers it. Generally, we show it can flexibly extract inductive biases from supervised learners, including spiking neural networks, and show how it could be applied to real animals. Finally, we use our tool to interpret recent connectomic data illustrating its intended use: understanding the role of circuit features through the resulting inductive bias.
Long-Term Decrease in Coloration: A Consequence of Climate Change?
Authors: David López-Idiáquez, Céline Teplitsky, Arnaud Grégoire, Amélie Fargevieille, María del Rey, Christophe de Franceschi, Anne Charmantier, Claire Doutrelant
Date: 2022-11-24 00:00:00

Climate change has been shown to affect fitness-related traits in a wide range of taxa; for instance, warming leads to phenological advancements in many plant and animal species. The influence of climate change on social and secondary sexual traits, that are associated with fitness due to their role as quality signals, is however unknown. Here, we use more than 5800 observations collected on two Mediterranean blue tit subspecies (Cyanistes caeruleus caeruleus and C.c. ogliastrae) to explore whether blue crown and yellow breast patch colourations have changed over the past 15 years. Our data suggests that colouration has become duller and less chromatic in both sexes. In addition, in the Corsican C.c. ogliastrae, but not in the mainland C.c. caeruleus, the decrease is associated with an increase in temperature at moult. Quantitative genetic analyses do not reveal any microevolutionary change in the colour traits along the study period, strongly suggesting that the observed change over time was caused by a plastic response to the environmental conditions. Overall, this study suggests that ornamental colourations could become less conspicuous due to warming, revealing climate change effects on sexual and social ornaments and calling for further research on the proximate mechanisms behind these effects.
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks
Authors: Kishan Kc, Rui Li, Paribesh Regmi, Anne R. Haake
Date: 2022-11-22 00:00:00
Tasks: Model Selection

A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. However, current biological networks are noisy, sparse, and incomplete, limiting our ability to create a holistic view of the biological system and understand the biological phenomena. Experimental identification of such interactions is both time-consuming and expensive. With the recent advancements in high-throughput data generation and significant improvement in computational power, various computational methods have been developed to predict novel interactions in the noisy network. Recently, deep learning methods such as graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. However, graph neural networks-based methods require human expertise and experimentation to design the appropriate complexity of the model and significantly impact the performance of the model. Furthermore, deep graph neural networks face overfitting problems and tend to be poorly calibrated with high confidence on incorrect predictions. To address these challenges, we propose Bayesian model selection for graph convolutional networks to jointly infer the most plausible number of graph convolution layers (depth) warranted by data and perform dropout regularization simultaneously. Experiments on four interaction datasets show that our proposed method achieves accurate and calibrated predictions. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.