pwc-archive/papers-with-abstracts huggingface dataset:
Mining Artifacts in Mycelium SEM Micrographs
Authors: Thaicia Stona de Almeida
Date: 2021-03-12 00:00:00
Tasks: BIG-bench Machine Learning

Mycelium is a promising biomaterial based on fungal mycelium, a highly porous, nanofibrous structure. Scanning electron micrographs are used to characterize its network, but the currently available tools for nanofibrous microstructures do not contemplate the particularities of biomaterials. The adoption of a software for artificial nanofibrous in mycelium characterization adds the uncertainty of imaging artifact formation to the analysis. The reported work combines supervised and unsupervised machine learning methods to automate the identification of artifacts in the mapped pores of mycelium microstructure. Keywords: Machine learning; unsupervised learning; image processing; mycelium; microstructure informatics
Untrained networks for compressive lensless photography
Authors: Kristina Monakhova, Vi Tran, Grace Kuo, Laura Waller
Date: 2021-03-13 00:00:00
Tasks: Compressive Sensing

Compressive lensless imagers enable novel applications in an extremely compact device, requiring only a phase or amplitude mask placed close to the sensor. They have been demonstrated for 2D and 3D microscopy, single-shot video, and single-shot hyperspectral imaging; in each of these cases, a compressive-sensing-based inverse problem is solved in order to recover a 3D data-cube from a 2D measurement. Typically, this is accomplished using convex optimization and hand-picked priors. Alternatively, deep learning-based reconstruction methods offer the promise of better priors, but require many thousands of ground truth training pairs, which can be difficult or impossible to acquire. In this work, we propose the use of untrained networks for compressive image recovery. Our approach does not require any labeled training data, but instead uses the measurement itself to update the network weights. We demonstrate our untrained approach on lensless compressive 2D imaging as well as single-shot high-speed video recovery using the camera's rolling shutter, and single-shot hyperspectral imaging. We provide simulation and experimental verification, showing that our method results in improved image quality over existing methods.
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks
Authors: Jingshuai Liu, Mehrdad Yaghoobi
Date: 2021-03-13 00:00:00
Tasks: compressed sensing, Deep Learning, Generative Adversarial Network, Image Generation, MRI Reconstruction

Compressed sensing (CS) leverages the sparsity prior to provide the foundation for fast magnetic resonance imaging (fastMRI). However, iterative solvers for ill-posed problems hinder their adaption to time-critical applications. Moreover, such a prior can be neither rich to capture complicated anatomical structures nor applicable to meet the demand of high-fidelity reconstructions in modern MRI. Inspired by the state-of-the-art methods in image generation, we propose a novel attention-based deep learning framework to provide high-quality MRI reconstruction. We incorporate large-field contextual feature integration and attention selection in a generative adversarial network (GAN) framework. We demonstrate that the proposed model can produce superior results compared to other deep learning-based methods in terms of image quality, and relevance to the MRI reconstruction in an extremely low sampling rate diet.
VMAF And Variants: Towards A Unified VQA
Authors: Pankaj Topiwala, Wei Dai, Jiangfeng Pian, Katalina Biondi, Arvind Krovvidi
Date: 2021-03-13 00:00:00
Tasks: feature selection, regression, Video Quality Assessment, Visual Question Answering (VQA)

Video quality assessment (VQA) is now a fast-growing subject, maturing in the full reference (FR) case, yet challenging in the exploding no reference (NR) case. We investigate variants of the popular VMAF video quality assessment algorithm for the FR case, using both support vector regression and feedforward neural networks. We extend it to the NR case, using some different features but similar learning, to develop a partially unified framework for VQA. When fully trained, FR algorithms such as VMAF perform very well on test datasets, reaching 90%+ match in PCC and SRCC; but for predicting performance in the wild, we train/test from scratch for each database. With an 80/20 train/test split, we still achieve about 90% performance on average in both PCC and SRCC, with up to 7-9% gains over VMAF, using an improved motion feature and better regression. Moreover, we even get decent performance (about 75%) if we ignore the reference, treating FR as NR, partly justifying our attempts at unification. In the true NR case, we reduce complexity vs. leading recent algorithms VIDEVAL, RAPIQUE, yet achieve performance within 3-5%. Moreover, we develop a method to analyze the saliency of features, and conclude that for both VIDEVAL and RAPIQUE, a small subset of their features are providing the bulk of the performance. In short, we find encouraging improvements in trainability in FR, while constraining training complexity against leading methods in NR, elucidating the saliency of features for feature selection.
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles
Authors: Su Pang, Hayder Radha
Date: 2021-03-13 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Multi-Object Tracking, Object Tracking

The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (MOT) in autonomous driving applications reside in the inherent uncertainties regarding the number of objects, when and where the objects may appear and disappear, and uncertainties regarding objects' states. Random finite set (RFS) based approaches can naturally model these uncertainties accurately and elegantly, and they have been widely used in radar-based tracking applications. In this work, we developed an RFS-based MOT framework for 3D LiDAR data. In partiuclar, we propose a Poisson multi-Bernoulli mixture (PMBM) filter to solve the amodal MOT problem for autonomous driving applications. To the best of our knowledge, this represents a first attempt for employing an RFS-based approach in conjunction with 3D LiDAR data for MOT applications with comprehensive validation using challenging datasets made available by industry leaders. The superior experimental results of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate that an RFS-based tracker outperforms many state-of-the-art deep learning-based and Kalman filter-based methods, and consequently, these results indicate a great potential for further exploration of RFS-based frameworks for 3D MOT applications.
Multi-view data capture for dynamic object reconstruction using handheld augmented reality mobiles
Authors: M. Bortolon, L. Bazzanella, F. Poiesi
Date: 2021-03-14 00:00:00
Tasks: 3D Reconstruction, Object Reconstruction

We propose a system to capture nearly-synchronous frame streams from multiple and moving handheld mobiles that is suitable for dynamic object 3D reconstruction. Each mobile executes Simultaneous Localisation and Mapping on-board to estimate its pose, and uses a wireless communication channel to send or receive synchronisation triggers. Our system can harvest frames and mobile poses in real time using a decentralised triggering strategy and a data-relay architecture that can be deployed either at the Edge or in the Cloud. We show the effectiveness of our system by employing it for 3D skeleton and volumetric reconstructions. Our triggering strategy achieves equal performance to that of an NTP-based synchronisation approach, but offers higher flexibility, as it can be adjusted online based on application needs. We created a challenging new dataset, namely 4DM, that involves six handheld augmented reality mobiles recording an actor performing sports actions outdoors. We validate our system on 4DM, analyse its strengths and limitations, and compare its modules with alternative ones.
Progressive residual learning for single image dehazing
Authors: Yudong Liang, Bin Wang, Jiaying Liu, Deyu Li, Yuhua Qian, Wenqi Ren
Date: 2021-03-14 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when applied to real scenarios due to the unavailable or insufficient data problems. On the other hand, the physical model-based methods have better interpretability but suffer from multi-objective optimizations of parameters, which may lead to sub-optimal dehazing results. In this paper, a progressive residual learning strategy has been proposed to combine the physical model-free dehazing process with reformulated scattering model-based dehazing operations, which enjoys the merits of dehazing methods in both categories. Specifically, the global atmosphere light and transmission maps are interactively optimized with the aid of accurate residual information and preliminary dehazed restorations from the initial physical model-free dehazing process. The proposed method performs favorably against the state-of-the-art methods on public dehazing benchmarks with better model interpretability and adaptivity for complex hazy data.
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
Authors: Anas M. Tahir, Muhammad E. H. Chowdhury, Amith Khandakar, Tawsifur Rahman, Yazan Qiblawey, Uzair Khurshid, Serkan Kiranyaz, Nabil Ibtehaz, M Shohel Rahman, Somaya Al-Madeed, Khaled Hameed, Tahir Hamid, Sakib Mahmud, Maymouna Ezeddin
Date: 2021-03-14 00:00:00
Tasks: Segmentation, Specificity

Coronavirus disease 2019 (COVID-19) has been the main agenda of the whole world, since it came into sight in December 2019 as it has significantly affected the world economy and healthcare system. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved astonishing performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by a novel human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an extensive iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.
MBAPose: Mask and Bounding-Box Aware Pose Estimation of Surgical Instruments with Photorealistic Domain Randomization
Authors: Masakazu Yoshimura, Murilo Marques Marinho, Kanako Harada, Mamoru Mitsuishi
Date: 2021-03-15 00:00:00
Tasks: 3D Pose Estimation, 6D Pose Estimation using RGB, Pose Estimation, Translation

Surgical robots are usually controlled using a priori models based on the robots' geometric parameters, which are calibrated before the surgical procedure. One of the challenges in using robots in real surgical settings is that those parameters can change over time, consequently deteriorating control accuracy. In this context, our group has been investigating online calibration strategies without added sensors. In one step toward that goal, we have developed an algorithm to estimate the pose of the instruments' shafts in endoscopic images. In this study, we build upon that earlier work and propose a new framework to more precisely estimate the pose of a rigid surgical instrument. Our strategy is based on a novel pose estimation model called MBAPose and the use of synthetic training data. Our experiments demonstrated an improvement of 21 % for translation error and 26 % for orientation error on synthetic test data with respect to our previous work. Results with real test data provide a baseline for further research.
I-Nema: A Biological Image Dataset for Nematode Recognition
Authors: Xuequan Lu, Yihao Wang, Sheldon Fung, Xue Qing
Date: 2021-03-15 00:00:00

Nematode worms are one of most abundant metazoan groups on the earth, occupying diverse ecological niches. Accurate recognition or identification of nematodes are of great importance for pest control, soil ecology, bio-geography, habitat conservation and against climate changes. Computer vision and image processing have witnessed a few successes in species recognition of nematodes; however, it is still in great demand. In this paper, we identify two main bottlenecks: (1) the lack of a publicly available imaging dataset for diverse species of nematodes (especially the species only found in natural environment) which requires considerable human resources in field work and experts in taxonomy, and (2) the lack of a standard benchmark of state-of-the-art deep learning techniques on this dataset which demands the discipline background in computer science. With these in mind, we propose an image dataset consisting of diverse nematodes (both laboratory cultured and naturally isolated), which, to our knowledge, is the first time in the community. We further set up a species recognition benchmark by employing state-of-the-art deep learning networks on this dataset. We discuss the experimental results, compare the recognition accuracy of different networks, and show the challenges of our dataset. We make our dataset publicly available at: https://github.com/xuequanlu/I-Nema
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution
Authors: Wenbin Xie, Dehua Song, Chang Xu, Chunjing Xu, HUI ZHANG, Yunhe Wang
Date: 2021-03-15 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Super-Resolution, Super-Resolution

Deep learning based methods, especially convolutional neural networks (CNNs) have been successfully applied in the field of single image super-resolution (SISR). To obtain better fidelity and visual quality, most of existing networks are of heavy design with massive computation. However, the computation resources of modern mobile devices are limited, which cannot easily support the expensive cost. To this end, this paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Since pixels or image patches belong to low-frequency areas contain relatively few textural details, this dynamic network will not affect the quality of resulting super-resolution images. In addition, we embed predictors into the proposed dynamic network to end-to-end fine-tune the handcrafted frequency-aware masks. Extensive experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures to obtain the better tradeoff between visual quality and computational complexity. For instance, we can reduce the FLOPs of SR models by approximate 50% while preserving state-of-the-art SISR performance.
DiaRet: A browser-based application for the grading of Diabetic Retinopathy with Integrated Gradients
Authors: Shaswat Patel, Maithili Lohakare, Samyak Prajapati, Shaanya Singh, Nancy Patel
Date: 2021-03-15 00:00:00
Tasks: Diagnostic

Patients with long-standing diabetes often fall prey to Diabetic Retinopathy (DR) resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image. In this research work, we have emulated the images plagued by distortions by degrading the images based on multiple different combinations of Light Transmission Disturbance, Image Blurring and insertion of Retinal Artifacts. InceptionV3, ResNet-50 and InceptionResNetV2 were trained and used to classify retinal fundus images based on their severity level and then further used in the creation of a browser-based application, which implements the Integration Gradient (IG) Attribution Mask on the input image and demonstrates the predictions made by the model and the probability associated with each class.
Light chain systemic amyloidosis manifested as liver failure complicated with fatal spontaneous splenic rupture: A case report
Authors: Li Duo, Zhao Yingren, Chen Hongmei
Date: 2021-03-13 00:00:00
Tasks: Prognosis

For a patient with manifestations of nausea, abdominal distension, spontaneous splenic rupture, obvious liver enlargement, low red blood cells and platelets, yellow sclera, and spider angioma, Congo red staining of liver and spleen tissues indicated amyloidosis. After secondary factors were excluded, the patient was finally diagnosed as chronic liver failure, light chain amyloidosis, spontaneous bacterial peritonitis after cystic resection and splenectomy. This case suggests that for patients with chronic liver failure accompanied by spontaneous splenic rupture and hepatomegaly for unknown reasons, the possibility of amyloidosis should be considered after excluding other factors, such as viral liver disease, autoimmune disease, alcoholic liver disease, genetic metabolic liver disease and liver tumor, and etc. Considering the low clinical incidence rate and poor prognosis, relevant diagnosis depends on the biopsy results, and many patients were not confirmed until autopsy after death. Therefore, once amyloidosis is suspected, it is necessary to have communications with relevant patients and their families on the risks for examination, treatment methods and prognosis as soon as possible.
Neural Knapsack: A Neural Network Based Solver for the Knapsack Problem
Authors: Hazem A. A. Nomer, Khalid Abdulaziz Alnowibet, Ashraf Elsayed, Ali Wagdy Mohamed
Date: 2020-12-11 00:00:00
Tasks: Combinatorial Optimization

This paper introduces a heuristic solver based on neural networks and deep learning for the knapsack problem. The solver is inspired by mechanisms and strategies used by both algorithmic solvers and humans. The neural model of the solver is based on introducing several biases in the architecture. We introduce a stored memory of vectors that holds up items representations and their relationship to the capacity of the knapsack and a module that allows the solver to access all the previous outputs it generated. The solver is trained and tested on synthetic datasets that represent a variety of instance types with different complexities. The solver neural model capabilities to generalize were tested on instances with up to 200 items, the model succeed to obtain near optimal solutions better than the greedy algorithm for instances in which there exists a correlation between items values and weights. The results also show that the capacity of the knapsack has a role in learning useful representations for each item in an instance and for the instance itself. Although the proposed solver may be not superior to other solvers, the results described here are insights for how the connection between combinatorial optimization, machine learning, and cognitive science could serve a great purpose in the operation research field. The goal of this work is not to design a state of the art solver, rather it full-fills some of the holes in the recent line of research that incorporates learning in combinatorial optimization problems.
Method for Registration of 3D Shapes Without Overlap for Known 3D Priors
Authors: Pengpeng Hu, Adrian Munteanu
Date: 2021-03-15 00:00:00

In 3D registration of point clouds, the goal is to find an optimal transformation
that aligns the input shapes, provided that they have some
overlap. Existing methods suffer from performance degradation when
the overlapping ratio between the neighbouring point clouds is small.
So far, there is no existing method that can be adopted for aligning
shapes with no overlap. In this letter, to the best of knowledge, the
first method for the registration of 3D shapes without overlap, assuming
that the shapes correspond to partial views of a known semi-rigid
3D prior is presented. The method is validated and compared to existing
methods on FAUST, which is a known dataset used for human body
reconstruction. Experimental results show that this approach can effectively
align shapes without overlap. Compared to existing state-of-theart
methods, this approach avoids iterative optimization and is robust to
outliers and inherent inaccuracies induced by an initial rough alignment
of the shapes.
Brain age prediction using deep learning uncovers associated sequence variants
Authors: B. A. Jonsson, G. Bjornsdottir, T. E. Thorgeirsson, L. M. Ellingsen, G. Bragi Walters, D. F. Gudbjartsson, H. Stefansson, K. Stefansson, M. O. Ulfarsson
Date: 2019-11-27 00:00:00
Tasks: Age Estimation, Deep Learning, Transfer Learning

Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: N=12378, replication set: N=4456) yielded two sequence variants, rs1452628-T (β=−0.08, P=1.15×10−9) and rs2435204-G (β=0.102, P=9.73×10−12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).