pwc-archive/papers-with-abstracts huggingface dataset:
IMIL: Interactive Medical Image Learning Framework
Authors: Adrit Rao, Andrea Fisher, Ken Chang, John Christopher Panagides, Katherine McNamara, Joon-Young Lee, Oliver Aalami
Date: 2024-04-17 00:00:00
Tasks: Diversity, Medical Image Analysis

Data augmentations are widely used in training medical image deep learning models to increase the diversity and size of sparse datasets. However, commonly used augmentation techniques can result in loss of clinically relevant information from medical images, leading to incorrect predictions at inference time. We propose the Interactive Medical Image Learning (IMIL) framework, a novel approach for improving the training of medical image analysis algorithms that enables clinician-guided intermediate training data augmentations on misprediction outliers, focusing the algorithm on relevant visual information. To prevent the model from using irrelevant features during training, IMIL will 'blackout' clinician-designated irrelevant regions and replace the original images with the augmented samples. This ensures that for originally mispredicted samples, the algorithm subsequently attends only to relevant regions and correctly correlates them with the respective diagnosis. We validate the efficacy of IMIL using radiology residents and compare its performance to state-of-the-art data augmentations. A 4.2% improvement in accuracy over ResNet-50 was observed when using IMIL on only 4% of the training set. Our study demonstrates the utility of clinician-guided interactive training to achieve meaningful data augmentations for medical image analysis algorithms.
Adapting SAM for Surgical Instrument Tracking and Segmentation in Endoscopic Submucosal Dissection Videos
Authors: Jieming Yu, Long Bai, Guankun Wang, An Wang, Xiaoxiao Yang, Huxin Gao, Hongliang Ren
Date: 2024-04-16 00:00:00
Tasks: Segmentation

The precise tracking and segmentation of surgical instruments have led to a remarkable enhancement in the efficiency of surgical procedures. However, the challenge lies in achieving accurate segmentation of surgical instruments while minimizing the need for manual annotation and reducing the time required for the segmentation process. To tackle this, we propose a novel framework for surgical instrument segmentation and tracking. Specifically, with a tiny subset of frames for segmentation, we ensure accurate segmentation across the entire surgical video. Our method adopts a two-stage approach to efficiently segment videos. Initially, we utilize the Segment-Anything (SAM) model, which has been fine-tuned using the Low-Rank Adaptation (LoRA) on the EndoVis17 Dataset. The fine-tuned SAM model is applied to segment the initial frames of the video accurately. Subsequently, we deploy the XMem++ tracking algorithm to follow the annotated frames, thereby facilitating the segmentation of the entire video sequence. This workflow enables us to precisely segment and track objects within the video. Through extensive evaluation of the in-distribution dataset (EndoVis17) and the out-of-distribution datasets (EndoVis18 & the endoscopic submucosal dissection surgery (ESD) dataset), our framework demonstrates exceptional accuracy and robustness, thus showcasing its potential to advance the automated robotic-assisted surgery.
Restoring Connectivity in Vascular Segmentation using a Learned Post-Processing Model
Authors: Sophie Carneiro-Esteves, Antoine Vacavant, Odyssée Merveille
Date: 2024-04-16 00:00:00
Tasks: Segmentation

Accurate segmentation of vascular networks is essential for computer-aided tools designed to address cardiovascular diseases. Despite more than thirty years of research, it remains a challenge to obtain vascular segmentation results that preserve the connectivity of the underlying vascular network. Yet connectivity is one of the key feature of these tools. In this work, we propose a post-processing algorithm aiming to reconnect vascular structures that have been disconnected by a segmentation algorithm. Connectivity being a complex property to model explicity, we propose to learn this geometric feature either through synthetic data or annotations of the application of interest. The resulting post-processing model can be used on the output of any supervised or unsupervised vascular segmentation algorithm. We show that this post-processing effectively restores the connectivity of vascular networks both in 2D and 3D images, leading to improved overall segmentation results.
Diffusion assisted image reconstruction in optoacoustic tomography
Authors: M. G. González, M. Vera, A. Dreszman, L. J. Rey Vega
Date: 2024-04-16 00:00:00
Tasks: Image Reconstruction

In this paper we consider the problem of acoustic inversion in the context of the optoacoustic tomography image reconstruction problem. By leveraging the ability of the recently proposed diffusion models for image generative tasks among others, we devise an image reconstruction architecture based on a conditional diffusion process. The scheme makes use of an initial image reconstruction, which is preprocessed by an autoencoder to generate an adequate representation. This representation is used as conditional information in a generative diffusion process. Although the computational requirements for training and implementing the architecture are not low, several design choices discussed in the work were made to keep them manageable. Numerical results show that the conditional information allows to properly bias the parameters of the diffusion model to improve the quality of the initial reconstructed image, eliminating artifacts or even reconstructing finer details of the ground-truth image that are not recoverable by the initial image reconstruction method. We also tested the proposal under experimental conditions and the obtained results were in line with those corresponding to the numerical simulations. Improvements in image quality up to 17 % in terms of peak signal-to-noise ratio were observed.
Classifying Sums of Exponentially Damped Sinusoids Using an Associated Numerical Range
Authors: Raymundo Albert, Cecilia G. Galarza
Date: 2024-04-17 00:00:00
Tasks: Classification

The matrix pencil method (MPM) is a well-known technique for estimating the parameters of exponentially damped sinusoids in noise by solving a generalized eigenvalue problem. However, in several cases, this is an ill-conditioned problem whose solution is highly biased under small perturbations. When the estimation is performed to classify the observed signal into two categories, the estimation errors induce several misclassifications. In this work we propose a novel signal classification criteria by exploiting the relationship between the generalized eigenvalue problem posed in the MPM and the numerical range of a pair of rectangular matrices. In particular, the classification test is formulated as a set inclusion problem, and no spectrum estimation is required. The technique is applied to a problem of electromagnetic scattering to classify dielectric materials using the scattering signal observed when a target is illuminated by an ultra-wideband signal. The performance of the classification scheme is assessed in terms of error rate and it is compared to another classification technique, the generalized likelihood rate test (GLRT).
Robust and tractable multidimensional exponential analysis
Authors: H. N. Mhaskar, S. Kitimoon, Raghu G. Raj
Date: 2024-04-17 00:00:00

Motivated by a number of applications in signal processing, we study the following question. Given samples of a multidimensional signal of the form \begin{align*} f(\bs\ell)=\sum_{k=1}^K a_k\exp(-i\langle \bs\ell, \w_k\rangle), \\ \w_1,\cdots,\w_k\in\mathbb{R}^q, \ \bs\ell\in \ZZ^q, \ |\bs\ell| <n, \end{align*} determine the values of the number $K$ of components, and the parameters $a_k$ and $\w_k$'s. We develop an algorithm to recuperate these quantities accurately using only a subsample of size $\O(qn)$ of this data. For this purpose, we use a novel localized kernel method to identify the parameters, including the number $K$ of signals. Our method is easy to implement, and is shown to be stable under a very low SNR range. We demonstrate the effectiveness of our resulting algorithm using 2 and 3 dimensional examples from the literature, and show substantial improvements over state-of-the-art techniques including Prony based, MUSIC and ESPRIT approaches.
Ray-Tracing Calibration from Channel Sounding Measurements in a Millimeter-Wave Industrial Scenario
Authors: Grégory Gougeon, Frédéric Munoz, Yoann Corre, Raffaele D'Errico
Date: 2024-04-16 00:00:00

New-generation communication and sensing systems are gaining strong interest in the context of Industry 4.0 e.g., related to mapping techniques, environmental sensing, automation or hyper-vision. The radio propagation in confined, cluttered and heavily metalized factory environments is a critical challenge; thus an evaluation by accurate propagation channel models is necessary. Site-specific channel emulation can be obtained from Ray-tracing (RT); but RT validation for factory environments is still an on-going work. For this purpose, a measurement campaign was performed in a machine room with many metallic objects and machines, using a mmWave channel sounder. Wideband channel responses were collected and compared to RT simulations. The RT prediction tool was calibrated to minimize the error observed on some large scale statistics, thus reaching a very good agreement between the simulation and the measurement. Average error in received power, delay spread and azimuth spread is below 1.5 dB, 5 ns and 2{\deg} respectively.
Extended Automatic Repeat Request For Integrated Sensing And Communication Networks
Authors: Georgios Mylonopoulos, Behrooz Makki, Stefano Buzzi, Gábor Fodor
Date: 2024-04-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

6G wireless networks will integrate communication, computing, localization, and sensing capabilities while meeting the needs of high reliability and trustworthiness. In this paper, we develop similar techniques as those used by communication modules of previous generations for the sensing functionality of 6G networks. Specifically, this paper introduces the concept of extended automatic repeat request (e-ARQ) for integrated sensing and communications (ISAC) networks. We focus on multi-static sensing schemes, in which the nodes receiving the reflected sensing signals provide the transmitting nodes with configurable levels of feedback about the sensing result. This technique improves the sensing quality via retransmissions using adaptive parameters. We show that our proposed e-ARQ boosts the sensing quality in terms of detection accuracy and provides a sense of adaptability for applications supported by ISAC networks.
Worst-Case Riemannian Optimization with Uncertain Target Steering Vector for Slow-Time Transmit Sequence of Cognitive Radar
Authors: Xinyu Zhang, Weidong Jiang, Xiangfeng Qiu, Yongxiang Liu
Date: 2024-04-16 00:00:00
Tasks: Riemannian optimization

Optimization of slow-time transmit sequence endows cognitive radar with the ability to suppress strong clutter in the range-Doppler domain. However, in practice, inaccurate target velocity information or random phase error would induce uncertainty about the actual target steering vector, which would in turn severely deteriorate the the performance of the slow-time matched filter. In order to solve this problem, we propose a new optimization method for slow-time transmit sequence design. The proposed method transforms the original non-convex optimization with an uncertain target steering vector into a two-step worst-case optimization problem. For each sub-problem, we develop a corresponding trust-region Riemannian optimization algorithm. By iteratively solving the two sub-problems, a sub-optimal solution can be reached without accurate information about the target steering vector. Furthermore, the convergence property of the proposed algorithms has been analyzed and detailed proof of the convergence is given. Unlike the traditional waveform optimization method, the proposed method is designed to work with an uncertain target steering vector and therefore, is more robust in practical radar systems. Numerical simulation results in different scenarios verify the effectiveness of the proposed method in suppressing the clutter and show its advantages in terms of the output signal-to-clutter plus noise ratio (SCNR) over traditional methods.
Robust Snapshot Radio SLAM
Authors: Ossi Kaltiokallio, Elizaveta Rastorgueva-Foi, Jukka Talvitie, Yu Ge, Henk Wymeersch, Mikko Valkama
Date: 2024-04-16 00:00:00
Tasks: Simultaneous Localization and Mapping

The intrinsic geometric connections between millimeter-wave (mmWave) signals and the propagation environment can be leveraged for simultaneous localization and mapping (SLAM) in 5G and beyond networks. However, estimated channel parameters that are mismatched to the utilized geometric model can cause the SLAM solution to degrade. In this paper, we propose a robust snapshot radio SLAM algorithm for mixed line-of-sight (LoS) and non-line-of-sight (NLoS) environments that can estimate the unknown user equipment (UE) state, map of the environment as well as the presence of the LoS path. The proposed method can accurately detect outliers and the LoS path, enabling robust estimation in both LoS and NLoS conditions. The proposed method is validated using 60 GHz experimental data, indicating superior performance compared to the state-of-the-art.
Integrated Sensing and Communication for Edge Inference with End-to-End Multi-View Fusion
Authors: Xibin Jin, Guoliang Li, Shuai Wang, Miaowen Wen, Chengzhong Xu, H. Vincent Poor
Date: 2024-04-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals. However, this paradigm needs to minimize the inference error and latency under ISAC co-functionality interference, for which the existing ISAC or edge resource allocation algorithms become inefficient, as they ignore the inter-dependency between low-level ISAC designs and high-level inference services. This letter proposes an inference-oriented ISAC (IO-ISAC) scheme, which minimizes upper bounds on end-to-end inference error and latency using multi-objective optimization. The key to our approach is to derive a multi-view inference model that accounts for both the number of observations and the angles of observations, by integrating a half-voting fusion rule and an angle-aware sensing model. Simulation results show that the proposed IO-ISAC outperforms other benchmarks in terms of both accuracy and latency.
Little Pilot is Needed for Channel Estimation with Integrated Super-Resolution Sensing and Communication
Authors: Jingran Xu, Huizhi Wang, Yong Zeng, Xiaoli Xu
Date: 2024-04-16 00:00:00
Tasks: parameter estimation, Super-Resolution

Integrated super-resolution sensing and communication (ISSAC) is a promising technology to achieve extremely high sensing performance for critical parameters, such as the angles of the wireless channels. In this paper, we propose an ISSAC-based channel estimation method, which requires little or even no pilot, yet still achieves accurate channel state information (CSI) estimation. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) do not require a priori known pilots for accurate parameter estimation. Therefore, in the proposed method, the angles of the multi-path channel components are first estimated in a pilot-free manner while communication data symbols are sent. After that, the multi-path channel coefficients are estimated, where very little pilots are needed. The reasons are two folds. First, compared to the conventional channel estimation methods purely relying on channel training, much fewer parameters need to be estimated once the multi-path angles are accurately estimated. Besides, with angles obtained, the beamforming gain is also enjoyed when pilots are sent to estimate the channel path gains. To rigorously study the performance of the proposed method, we first consider the basic line-of-sight (LoS) channel. By analyzing the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains, we show that our proposed method significantly outperforms the conventional methods purely based on channel training. We then extend the study to the more general multipath channels. Simulation results are provided to demonstrate our theoretical results.
Channel Estimation for AFDM With Superimposed Pilots
Authors: Kai Zheng, Miaowen Wen, Tianqi Mao, Lixia Xiao, Zhaocheng Wang
Date: 2024-04-16 00:00:00

The recent proposed affine frequency division multiplexing (AFDM) employing a multi-chirp waveform has shown its reliability and robustness in doubly selective fading channels. In the existing embedded pilot-aided channel estimation methods, the presence of guard symbols in the discrete affine Fourier transform (DAFT) domain causes inevitable degradation of the spectral efficiency (SE). To improve the SE, we propose a novel AFDM channel estimation scheme by introducing the superimposed pilots in the DAFT domain. An effective pilot placement method that minimizes the channel estimation error is also developed with a rigorous proof. To mitigate the pilot-data interference, we further propose an iterative channel estimator and signal detector. Simulation results demonstrate that both channel estimation and data detection performances can be improved by the proposed scheme as the number of superimposed pilots increases.
Are Medium-Sized Transformers Models still Relevant for Medical Records Processing?
Authors: Boammani Aser Lompo, Thanh-Dung Le, Philippe Jouvet, Rita Noumeir
Date: 2024-04-15 00:00:00

As large language models (LLMs) become the standard in many NLP applications, we explore the potential of medium-sized pretrained transformer models as a viable alternative for medical record processing. Medical records generated by healthcare professionals during patient admissions remain underutilized due to challenges such as complex medical terminology, the limited ability of pretrained models to interpret numerical data, and the scarcity of annotated training datasets. Objective: This study aims to classify numerical values extracted from medical records into seven distinct physiological categories using CamemBERT-bio. Previous research has suggested that transformer-based models may underperform compared to traditional NLP approaches in this context. Methods: To enhance the performance of CamemBERT-bio, we propose two key innovations: (1) incorporating keyword embeddings to refine the model's attention mechanisms and (2) adopting a number-agnostic strategy by removing numerical values from the text to encourage context-driven learning. Additionally, we assess the criticality of extracted numerical data by verifying whether values fall within established standard ranges. Results: Our findings demonstrate significant performance improvements, with CamemBERT-bio achieving an F1 score of 0.89 - an increase of over 20% compared to the 0.73 F1 score of traditional methods and only 0.06 units lower than GPT-4. These results were obtained despite the use of small and imbalanced training datasets.
Reconstructing classes of 3D FRI signals from sampled tomographic projections at unknown angles
Authors: Renke Wang, Francien G. Bossema, Thierry Blu, Pier Luigi Dragotti
Date: 2024-04-15 00:00:00
Tasks: Computed Tomography (CT)

Traditional sampling schemes often assume that the sampling locations are known. Motivated by the recent bioimaging technique known as cryogenic electron microscopy (cryoEM), we consider the problem of reconstructing an unknown 3D structure from samples of its 2D tomographic projections at unknown angles. We focus on 3D convex bilevel polyhedra and 3D point sources and show that the exact estimation of these 3D structures and of the projection angles can be achieved up to an orthogonal transformation. Moreover, we are able to show that the minimum number of projections needed to achieve perfect reconstruction is independent of the complexity of the signal model. By using the divergence theorem, we are able to retrieve the projected vertices of the polyhedron from the sampled tomographic projections, and then we show how to retrieve the 3D object and the projection angles from this information. The proof of our theorem is constructive and leads to a robust reconstruction algorithm, which we validate under various conditions. Finally, we apply aspects of the proposed framework to calibration of X-ray computed tomography (CT) data.
Custom frame synchronization for easy and rapid deployment
Authors: Dimitris Nikolaidis
Date: 2024-04-15 00:00:00
Tasks: valid

We propose a novel and efficient, custom frame synchronization architecture aimed at rapid deployment on any hardware platform. Frame synchronization is the process of discerning valid data frames from an incoming transmission and in this article it is accomplished by attaching distinctive binary overhead sequences on the frame. These sequences act as markers for the frames and enable the capture of their payload. They have certain properties and can be detected by using only simple hardware constructs like XNOR gates and few-bit adders with adequate accuracy. A low-cost commercial FPGA was used for implementation (NEXYS 4 DDR).
Bounding seed loss from isolated habitat patches
Authors: Benjamin Hafner, Katherine Meyer
Date: 2024-04-15 00:00:00

Dispersal of propagules (seeds, spores) from a geographically isolated population into an uninhabitable matrix can threaten population persistence if it prevents new growth from keeping pace with mortality. Quantifying propagule loss can thus inform restoration and conservation of vulnerable populations in fragmented landscapes. To model propagule loss in detail, one can integrate dispersal kernels (probabilistic descriptions of dispersal) and plant densities. However, one might lack the detailed spatial information and computational tools needed by such integral models. Here we derive two upper bounds on the probability of propagule loss--one assuming rotational symmetry of dispersal and the other not--that require only habitat area, habitat perimeter, and the mean dispersal distance of a propagule. We compare the bounds to simulations of integral models for the population of Asclepias syriaca (common milkweed) at McKnight Prairie--a 13.7 hectare reserve surrounded by agricultural fields in Goodhue County, Minnesota--and identify conditions under which the bounds closely estimate propagule loss.