pwc-archive/papers-with-abstracts huggingface dataset:
Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology
Authors: Milda Pocevičiūtė, Gabriel Eilertsen, Sofia Jarkman, Claes Lundström
Date: 2021-12-17 00:00:00
Tasks: Diagnostic

Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the effectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are compared. Our experiments focus on two domain shift scenarios: a shift to a different medical center and to an underrepresented subtype of cancer. Our results show that uncertainty estimates increase reliability by reducing a model's sensitivity to classification threshold selection as well as by detecting between 70\% and 90\% of the mispredictions done by the model. Overall, the deep ensembles method achieved the best performance closely followed by TTA.
Procedural Kernel Networks
Authors: Bartlomiej Wronski
Date: 2021-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Demosaicking, Denoising, Image Restoration

In the last decade Convolutional Neural Networks (CNNs) have defined the state of the art for many low level image processing and restoration tasks such as denoising, demosaicking, upscaling, or inpainting. However, on-device mobile photography is still dominated by traditional image processing techniques, and uses mostly simple machine learning techniques or limits the neural network processing to producing low resolution masks. High computational and memory requirements of CNNs, limited processing power and thermal constraints of mobile devices, combined with large output image resolutions (typically 8--12 MPix) prevent their wider application. In this work, we introduce Procedural Kernel Networks (PKNs), a family of machine learning models which generate parameters of image filter kernels or other traditional algorithms. A lightweight CNN processes the input image at a lower resolution, which yields a significant speedup compared to other kernel-based machine learning methods and allows for new applications. The architecture is learned end-to-end and is especially well suited for a wide range of low-level image processing tasks, where it improves the performance of many traditional algorithms. We also describe how this framework unifies some previous work applying machine learning for common image restoration tasks.
Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels
Authors: Mohammad H. Givkashi, Mahshid Hadipour, Arezoo PariZanganeh, Zahra Nabizadeh, Nader Karimi, Shadrokh Samavi
Date: 2021-12-17 00:00:00
Tasks: Image Inpainting

Image inpainting is an effective method to enhance distorted digital images. Different inpainting methods use the information of neighboring pixels to predict the value of missing pixels. Recently deep neural networks have been used to learn structural and semantic details of images for inpainting purposes. In this paper, we propose a network for image inpainting. This network, similar to U-Net, extracts various features from images, leading to better results. We improved the final results by replacing the damaged pixels with the recovered pixels of the output images. Our experimental results show that this method produces high-quality results compare to the traditional methods.
A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory
Authors: Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Date: 2021-12-16 00:00:00
Tasks: Denoising, Image Denoising

Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising. In recent years, with the growth of computing power, data-driven strategies exploiting the redundancy within patches extracted from one or several images to increase sparsity have become more prominent. This paper presents a novel image denoising algorithm exploiting such an image-dependent basis inspired by the quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics that can efficiently preserve the local structures of real images. The versatile nature of this adaptive basis extends the scope of its application to image-independent or image-dependent noise scenarios without any adjustment. We carry out a rigorous comparison with contemporary methods to demonstrate the denoising capability of the proposed algorithm regardless of the image characteristics, noise statistics and intensity. We illustrate the properties of the hyperparameters and their respective effects on the denoising performance, together with automated rules of selecting their values close to the optimal one in experimental setups with ground truth not available. Finally, we show the ability of our approach to deal with practical images denoising problems such as medical ultrasound image despeckling applications.
A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy
Authors: Garvit Goel, Jingyuan Qi, Wu-chun Feng, Guohua Cao
Date: 2021-12-16 00:00:00
Tasks: Diagnostic, Image Enhancement

We present a deep-learning based computing framework for fast-and-accurate CT (DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to improve the testing speed and accuracy of COVID-19 (plus its variants) via a DL-based approach for CT image enhancement and classification. The image enhancement network is adapted from DDnet, short for DenseNet and Deconvolution based network. To demonstrate its speed and accuracy, we evaluated DL-FACT across several sources of COVID-19 CT images. Our results show that DL-FACT can significantly shorten the turnaround time from days to minutes and improve the COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool for medical professionals in diagnosing and monitoring COVID-19.
Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning
Authors: Sina Malakpour Estalaki, Cody S. Lough, Robert G. Landers, Edward C. Kinzel, Tengfei Luo
Date: 2021-12-16 00:00:00
Tasks: Binary Classification, Feature Importance

Variation in the local thermal history during the laser powder bed fusion (LPBF) process in additive manufacturing (AM) can cause microporosity defects. in-situ sensing has been proposed to monitor the AM process to minimize defects, but the success requires establishing a quantitative relationship between the sensing data and the porosity, which is especially challenging for a large number of variables and computationally costly. In this work, we develop machine learning (ML) models that can use in-situ thermographic data to predict the microporosity of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold (/tau) and the maximum radiance (T_{max}). These features are computed, stored for each voxel in the built material, are used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for random forests. Feature importance analysis based on the ML models shows that T_{max}is more important to the voxel state than /tau. The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it.
An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification
Authors: Lam Pham, Dat Ngo, Phu X. Nguyen, Truong Hoang, Alexander Schindler
Date: 2021-12-16 00:00:00
Tasks: Deep Learning, Scene Classification

This paper presents a task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: 'Riot', 'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'. To this end, we firstly collect an audio-visual dataset (videos) of these five crowded contexts from Youtube (in-the-wild scenes). Then, a wide range of deep learning frameworks are proposed to deploy either audio or visual input data independently. Finally, results obtained from high-performed deep learning frameworks are fused to achieve the best accuracy score. Our experimental results indicate that audio and visual input factors independently contribute to the SC task's performance. Significantly, an ensemble of deep learning frameworks exploring either audio or visual input data can achieve the best accuracy of 95.7%.
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network
Authors: Raunak Dey, Wenbo Sun, Haibo Xu, Yi Hong
Date: 2021-12-16 00:00:00
Tasks: Anomaly Segmentation, Brain Tumor Segmentation, Lesion Segmentation, Novelty Detection, Segmentation, Tumor Segmentation

In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from experts and the existence of a large amount of unannotated normal and abnormal image scans. We introduce a segmentation network that utilizes adversarial learning to partition an image into two cuts, with one of them falling into a reference distribution provided by the user. This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep segmentation and adversarial-based anomaly/novelty detection algorithms. Our ASC-Net learns from normal and abnormal medical scans to segment anomalies in medical scans without any masks for supervision. We evaluate this unsupervised anomly segmentation model on three public datasets, i.e., BraTS 2019 for brain tumor segmentation, LiTS for liver lesion segmentation, and MS-SEG 2015 for brain lesion segmentation, and also on a private dataset for brain tumor segmentation. Compared to existing methods, our model demonstrates tremendous performance gains in unsupervised anomaly segmentation tasks. Although there is still room to further improve performance compared to supervised learning algorithms, the promising experimental results and interesting observations shed light on building an unsupervised learning algorithm for medical anomaly identification using user-defined knowledge.
Highly precise AMCW time-of-flight scanning sensor based on digital-parallel demodulation
Authors: Sung-Hyun Lee, Wook-Hyeon Kwon, Yoon-Seop Lim, Yong-Hwa Park
Date: 2021-12-16 00:00:00

In this paper, a novel amplitude-modulated continuous wave (AMCW) time-of-flight (ToF) scanning sensor based on digital-parallel demodulation is proposed and demonstrated in the aspect of distance measurement precision. Since digital-parallel demodulation utilizes a high-amplitude demodulation signal with zero-offset, the proposed sensor platform can maintain extremely high demodulation contrast. Meanwhile, as all cross correlated samples are calculated in parallel and in extremely short integration time, the proposed sensor platform can utilize a 2D laser scanning structure with a single photo detector, maintaining a moderate frame rate. This optical structure can increase the received optical SNR and remove the crosstalk of image pixel array. Based on these measurement properties, the proposed AMCW ToF scanning sensor shows highly precise 3D depth measurement performance. In this study, this precise measurement performance is explained in detail. Additionally, the actual measurement performance of the proposed sensor platform is experimentally validated under various conditions.
Transcoded Video Restoration by Temporal Spatial Auxiliary Network
Authors: Li Xu, Gang He, Jinjia Zhou, Jie Lei, Weiying Xie, Yunsong Li, Yu-Wing Tai
Date: 2021-12-15 00:00:00
Tasks: Video Editing, Video Restoration

In most video platforms, such as Youtube, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers. Previous works in compressed video restoration typically assume the compression artifacts are caused by one-time encoding. Thus, the derived solution usually does not work very well in practice. In this paper, we propose a new method, temporal spatial auxiliary network (TSAN), for transcoded video restoration. Our method considers the unique traits between video encoding and transcoding, and we consider the initial shallow encoded videos as the intermediate labels to assist the network to conduct self-supervised attention training. In addition, we employ adjacent multi-frame information and propose the temporal deformable alignment and pyramidal spatial fusion for transcoded video restoration. The experimental results demonstrate that the performance of the proposed method is superior to that of the previous techniques. The code is available at https://github.com/icecherylXuli/TSAN.
Depth-resolved vascular profile features for artery-vein classification in OCT and OCT angiography of human retina
Authors: Tobiloba Adejumo, Tae-hoon Kim, David Le, Taeyoon Son, Guangying Ma, Xincheng Yao
Date: 2021-12-14 00:00:00

This study is to characterize reflectance profiles of retinal blood vessels in optical coherence tomography (OCT), and to validate these vascular features to guide artery-vein classification in OCT angiography (OCTA) of human retina. Depth-resolved OCT reveals unique features of retinal arteries and veins. Retinal arteries show hyper-reflective boundaries at both upper (inner side towards the vitreous) and lower (outer side towards the choroid) walls. In contrary, retinal veins reveal hyper-reflectivity at the upper boundary only. Uniform lumen intensity was observed in both small and large arteries. However, the vein lumen intensity was dependent on the vessel size. Small veins exhibit a hyper-reflective zone at the bottom half of the lumen, while large veins show a hypo-reflective zone at the bottom half of the lumen
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications
Authors: Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stephane Tremblay, Alexander Wong
Date: 2021-12-14 00:00:00
Tasks: Medical Image Analysis, Representation Learning, Self-Supervised Learning

Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between performance and trust, both of which are essential for medical applications. Moreover, models optimized with cross-entropy loss tend to suffer from unwarranted overconfidence in the majority class and over-cautiousness in the minority class. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients using radiography images. In addition, we adopt a new quantification score to measure a model's trustworthiness. Ablation study is conducted for both the performance and the trust on feature learning methods and loss functions. Comparisons show that leveraging the new surrogate loss on self-supervised models can produce label-efficient networks that are both high-performing and trustworthy.
Mitigating Nonlinear Interference by Limiting Energy Variations in Sphere Shaping
Authors: Yunus Can Gültekin, Alex Alvarado, Olga Vassilieva, Inwoong Kim, Paparao Palacharla, Chigo Okonkwo, Frans M. J. Willems
Date: 2022-01-03 00:00:00

Band-trellis enumerative sphere shaping is proposed to decrease the energy variations in channel input sequences. Against sphere shaping, 0.74 dB SNR gain and up to 9% increase in data rates are demonstrated for single-span systems.
Data-driven Sensor Deployment for Spatiotemporal Field Reconstruction
Authors: Jiahong Chen
Date: 2022-01-02 00:00:00

This paper concerns the data-driven sensor deployment problem in large spatiotemporal fields. Traditionally, sensor deployment strategies have been heavily dependent on model-based planning approaches. However, model-based approaches do not typically maximize the information gain in the field, which tends to generate less effective sampling locations and lead to high reconstruction error. In the present paper, a data-driven approach is developed to overcome the drawbacks of the model-based approach and improve the spatiotemporal field reconstruction accuracy. The proposed method can select the most informative sampling locations to represent the entire spatiotemporal field. To this end, the proposed method decomposes the spatiotemporal field using principal component analysis (PCA) and finds the top r essential entities of the principal basis. The corresponding sampling locations of the selected entities are regarded as the sensor deployment locations. The observations collected at the selected sensor deployment locations can then be used to reconstruct the spatiotemporal field, accurately. Results are demonstrated using a National Oceanic and Atmospheric Administration sea surface temperature dataset. In the present study, the proposed method achieved the lowest reconstruction error among all methods.
Understanding Energy Efficiency and Interference Tolerance in Millimeter Wave Receivers
Authors: Panagiotis Skrimponis, Seongjoon Kang, Abbas Khalili, Wonho Lee, Navid Hosseinzadeh, Marco Mezzavilla, Elza Erkip, Mark J. W. Rodwell, James F. Buckwalter, Sundeep Rangan
Date: 2022-01-01 00:00:00

Power consumption is a key challenge in millimeter wave (mmWave) receiver front-ends, due to the need to support high dimensional antenna arrays at wide bandwidths. Recently, there has been considerable work in developing low-power front-ends, often based on low-resolution ADCs and low-power mixers. A critical but less studied consequence of such designs is the relatively low-dynamic range which in turn exposes the receiver to adjacent carrier interference and blockers. This paper provides a general mathematical framework for analyzing the performance of mmWave front-ends in the presence of out-of-band interference. The goal is to elucidate the fundamental trade-off of power consumption, interference tolerance and in-band performance. The analysis is combined with detailed network simulations in cellular systems with multiple carriers, as well as detailed circuit simulations of key components at 140 GHz. The analysis reveals critical bottlenecks for low-power interference robustness and suggests designs enhancements for use in practical systems.
Effect of Kinematics and Fluency in Adversarial Synthetic Data Generation for ASL Recognition with RF Sensors
Authors: M. M. Rahman, E. Malaia, A. C. Gurbuz, D. J. Griffin, C. Crawfordand S. Z. Gurbuz
Date: 2021-12-31 00:00:00
Tasks: Domain Adaptation, Synthetic Data Generation

RF sensors have been recently proposed as a new modality for sign language processing technology. They are non-contact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth, comparative examination of the kinematic properties of signing as measured by RF sensors for both fluent ASL users and hearing imitation signers. Second, as ASL recognition techniques utilizing deep learning requires a large amount of training data, this work examines the effect of signing kinematics and subject fluency on adversarial learning techniques for data synthesis. Two different approaches for the synthetic training data generation are proposed: 1) adversarial domain adaptation to minimize the differences between imitation signing and fluent signing data, and 2) kinematically-constrained generative adversarial networks for accurate synthesis of RF signing signatures. The results show that the kinematic discrepancies between imitation signing and fluent signing are so significant that training on data directly synthesized from fluent RF signers offers greater performance (93% top-5 accuracy) than that produced by adaptation of imitation signing (88% top-5 accuracy) when classifying 100 ASL signs.
Magnetoelectric Bio-Implants Powered and Programmed by a Single Transmitter for Coordinated Multisite Stimulation
Authors: Zhanghao Yu, Joshua C. Chen, Yan He, Fatima T. Alrashdan, Benjamin W. Avants, Amanda Singer, Jacob T. Robinson, Kaiyuan Yang
Date: 2021-12-31 00:00:00

This article presents a hardware platform including stimulating implants wirelessly powered and controlled by a shared transmitter (TX) for coordinated leadless multisite stimulation. The adopted novel single-TX, multiple-implant structure can flexibly deploy stimuli, improve system efficiency, easily scale stimulating channel quantity, and relieve efforts in device synchronization. In the proposed system, a wireless link leveraging magnetoelectric (ME) effect is co-designed with a robust and efficient system-on-chip (SoC) to enable reliable operation and individual programming of every implant. Each implant integrates a 0.8-mm2 chip, a 6-mm2 ME film, and an energy storage capacitor within a 6.2-mm3 size. ME power transfer is capable of safely transmitting milliwatt power to devices placed several centimeters away from the TX coil, maintaining good efficiency with size constraints, and tolerating 60 degree, 1.5-cm misalignment in angular and lateral movement. The SoC robustly operates with 2-V source amplitude variations that spans a 40-mm TX-implant distance change, realizes individual addressability through physical unclonable function (PUF) IDs, and achieves 90% efficiency for 1.5-3.5-V stimulation with fully programmable stimulation parameters.
WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting
Authors: Samer Hanna, Samurdhi Karunaratne, Danijela Cabric
Date: 2021-12-31 00:00:00

RF fingerprinting leverages circuit-level variability of transmitters to identify them using signals they send. Signals used for identification are impacted by a wireless channel and receiver circuitry, creating additional impairments that can confuse transmitter identification. Eliminating these impairments or just evaluating them, requires data captured over a prolonged period of time, using many spatially separated transmitters and receivers. In this paper, we present WiSig; a large scale WiFi dataset containing 10 million packets captured from 174 off-the-shelf WiFi transmitters and 41 USRP receivers over 4 captures spanning a month. WiSig is publicly available, not just as raw captures, but as conveniently pre-processed subsets of limited size, along with the scripts and examples. A preliminary evaluation performed using WiSig shows that changing receivers, or using signals captured on a different day can significantly degrade a trained classifier's performance. While capturing data over more days or more receivers limits the degradation, it is not always feasible and novel data-driven approaches are needed. WiSig provides the data to develop and evaluate these approaches towards channel and receiver agnostic transmitter fingerprinting.