pwc-archive/papers-with-abstracts huggingface dataset:
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure
Authors: Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W Sjoding
Date: 2021-08-27 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Diagnostic, Medical Diagnosis, Respiratory Failure

Objective: When patients develop acute respiratory failure, accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients. Materials and Methods: Machine learning models were trained to predict the common causes of acute respiratory failure (pneumonia, heart failure, and/or COPD). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort. Results: The internal cohort of 1,618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77-0.79), image model AUROC was 0.74 (0.72-0.75), and EHR model AUROC was 0.74 (0.70-0.76). For heart failure, combined: 0.83 (0.77-0.84), image: 0.80 (0.71-0.81), and EHR: 0.79 (0.75-0.82). For COPD, combined: AUROC = 0.88 (0.83-0.91), image: 0.83 (0.77-0.89), and EHR: 0.80 (0.76-0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia. Conclusions: Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of acute respiratory failure. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.
Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image
Authors: M. Nakao, F. Tong, M. Nakamura, T. Matsuda
Date: 2021-08-28 00:00:00

Shape reconstruction of deformable organs from two-dimensional X-ray images is a key technology for image-guided intervention. In this paper, we propose an image-to-graph convolutional network (IGCN) for deformable shape reconstruction from a single-viewpoint projection image. The IGCN learns relationship between shape/deformation variability and the deep image features based on a deformation mapping scheme. In experiments targeted to the respiratory motion of abdominal organs, we confirmed the proposed framework with a regularized loss function can reconstruct liver shapes from a single digitally reconstructed radiograph with a mean distance error of 3.6mm.
QACE: Asking Questions to Evaluate an Image Caption
Authors: Hwanhee Lee, Thomas Scialom, Seunghyun Yoon, Franck Dernoncourt, Kyomin Jung
Date: 2021-08-28 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Question Answering, Visual Question Answering (VQA)

In this paper, we propose QACE, a new metric based on Question Answering for Caption Evaluation. QACE generates questions on the evaluated caption and checks its content by asking the questions on either the reference caption or the source image. We first develop QACE-Ref that compares the answers of the evaluated caption to its reference, and report competitive results with the state-of-the-art metrics. To go further, we propose QACE-Img, which asks the questions directly on the image, instead of reference. A Visual-QA system is necessary for QACE-Img. Unfortunately, the standard VQA models are framed as a classification among only a few thousand categories. Instead, we propose Visual-T5, an abstractive VQA system. The resulting metric, QACE-Img is multi-modal, reference-less, and explainable. Our experiments show that QACE-Img compares favorably w.r.t. other reference-less metrics. We will release the pre-trained models to compute QACE.
An implementation of ROS Autonomous Navigation on Parallax Eddie platform
Authors: Hafiq Anas, Wee Hong Ong
Date: 2021-08-28 00:00:00
Tasks: Autonomous Navigation

This paper presents an implementation of autonomous navigation functionality based on Robot Operating System (ROS) on a wheeled differential drive mobile platform called Eddie robot. ROS is a framework that contains many reusable software stacks as well as visualization and debugging tools that provides an ideal environment for any robotic project development. The main contribution of this paper is the description of the customized hardware and software system setup of Eddie robot to work with an autonomous navigation system in ROS called Navigation Stack and to implement one application use case for autonomous navigation. For this paper, photo taking is chosen to demonstrate a use case of the mobile robot.
Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging
Authors: Ziyi Meng, Zhenming Yu, Kun Xu, Xin Yuan
Date: 2021-08-28 00:00:00
Proceeding: ICCV 2021 10
Tasks: Denoising

We consider using {\bf\em untrained neural networks} to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Various SCI systems have been built in recent years to capture data such as high-speed videos, hyperspectral images, and the state-of-the-art reconstruction is obtained by the deep neural networks. However, most of these networks are trained in an end-to-end manner by a large amount of corpus with sometimes simulated ground truth, measurement pairs. In this paper, inspired by the untrained neural networks such as deep image priors (DIP) and deep decoders, we develop a framework by integrating DIP into the plug-and-play regime, leading to a self-supervised network for spectral SCI reconstruction. Extensive synthetic and real data results show that the proposed algorithm without training is capable of achieving competitive results to the training based networks. Furthermore, by integrating the proposed method with a pre-trained deep denoising prior, we have achieved state-of-the-art results. {Our code is available at \url{https://github.com/mengziyi64/CASSI-Self-Supervised}.}
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression
Authors: Minsik Cho, Keivan A. Vahid, Saurabh Adya, Mohammad Rastegari
Date: 2021-08-28 00:00:00
Proceeding: ICLR 2022 4
Tasks: Clustering, Model Compression, Neural Network Compression

Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model compression. DKM casts k-means clustering as an attention problem and enables joint optimization of the DNN parameters and clustering centroids. Unlike prior works that rely on additional regularizers and parameters, DKM-based compression keeps the original loss function and model architecture fixed. We evaluated DKM-based compression on various DNN models for computer vision and natural language processing (NLP) tasks. Our results demonstrate that DKM delivers superior compression and accuracy trade-off on ImageNet1k and GLUE benchmarks. For example, DKM-based compression can offer 74.5% top-1 ImageNet1k accuracy on ResNet50 DNN model with 3.3MB model size (29.4x model compression factor). For MobileNet-v1, which is a challenging DNN to compress, DKM delivers 63.9% top-1 ImageNet1k accuracy with 0.72 MB model size (22.4x model compression factor). This result is 6.8% higher top-1accuracy and 33% relatively smaller model size than the current state-of-the-art DNN compression algorithms. Additionally, DKM enables compression of DistilBERT model by 11.8x with minimal (1.1%) accuracy loss on GLUE NLP benchmarks.
A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry
Authors: Yuan Gao, Lok Hin Lee, Richard Droste, Rachel Craik, Sridevi Beriwal, Aris Papageorghiou, Alison Noble
Date: 2021-08-28 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types. Our proposed method, which employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial pathways which enforce model invariance to; i) adversarial perturbations in the feature space derived from LC images, and ii) appearance domain discrepancy. Our Dual Adversarial Calibration method estimates transcerebellar diameter and head circumference on images from low-cost ultrasound devices with a mean absolute error (MAE) of 2.43mm and 1.65mm, compared with 7.28 mm and 5.65 mm respectively for SOTA.
CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models
Authors: Max Zvyagin, Thomas Brettin, Arvind Ramanathan, Sumit Kumar Jha
Date: 2021-08-29 00:00:00
Tasks: Deep Learning, Hyperparameter Optimization

The training of neural networks using different deep learning frameworks may lead to drastically differing accuracy levels despite the use of the same neural network architecture and identical training hyperparameters such as learning rate and choice of optimization algorithms. Currently, our ability to build standardized deep learning models is limited by the availability of a suite of neural network and corresponding training hyperparameter benchmarks that expose differences between existing deep learning frameworks. In this paper, we present a living dataset of models and hyperparameters, called CrossedWires, that exposes semantic differences between two popular deep learning frameworks: PyTorch and Tensorflow. The CrossedWires dataset currently consists of models trained on CIFAR10 images using three different computer vision architectures: VGG16, ResNet50 and DenseNet121 across a large hyperparameter space. Using hyperparameter optimization, each of the three models was trained on 400 sets of hyperparameters suggested by the HyperSpace search algorithm. The CrossedWires dataset includes PyTorch and Tensforflow models with test accuracies as different as 0.681 on syntactically equivalent models and identical hyperparameter choices. The 340 GB dataset and benchmarks presented here include the performance statistics, training curves, and model weights for all 1200 hyperparameter choices, resulting in 2400 total models. The CrossedWires dataset provides an opportunity to study semantic differences between syntactically equivalent models across popular deep learning frameworks. Further, the insights obtained from this study can enable the development of algorithms and tools that improve reliability and reproducibility of deep learning frameworks. The dataset is freely available at https://github.com/maxzvyagin/crossedwires through a Python API and direct download link.
Rethinking Deep Image Prior for Denoising
Authors: Yeonsik Jo, Se Young Chun, Jonghyun Choi
Date: 2021-08-29 00:00:00
Proceeding: ICCV 2021 10
Tasks: Denoising, Inductive Bias, SSIM

Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. We also propose the `stochastic temporal ensemble (STE)' method for incorporating techniques to further improve DIP's performance for denoising. We additionally extend our method to Poisson noise. Our empirical validations show that given a single noisy image, our method denoises the image while preserving rich textual details. Further, our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.
Lipschitz Continuity Guided Knowledge Distillation
Authors: Yuzhang Shang, Bin Duan, Ziliang Zong, Liqiang Nie, Yan Yan
Date: 2021-08-29 00:00:00
Proceeding: ICCV 2021 10
Tasks: Knowledge Distillation, Model Compression, object-detection, Object Detection

Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones. Although great success has been achieved by prior distillation methods via delicately designing various types of knowledge, they overlook the functional properties of neural networks, which makes the process of applying those techniques to new tasks unreliable and non-trivial. To alleviate such problem, in this paper, we initially leverage Lipschitz continuity to better represent the functional characteristic of neural networks and guide the knowledge distillation process. In particular, we propose a novel Lipschitz Continuity Guided Knowledge Distillation framework to faithfully distill knowledge by minimizing the distance between two neural networks' Lipschitz constants, which enables teacher networks to better regularize student networks and improve the corresponding performance. We derive an explainable approximation algorithm with an explicit theoretical derivation to address the NP-hard problem of calculating the Lipschitz constant. Experimental results have shown that our method outperforms other benchmarks over several knowledge distillation tasks (e.g., classification, segmentation and object detection) on CIFAR-100, ImageNet, and PASCAL VOC datasets.
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Authors: Myung-Joon Kwon, Seung-Hun Nam, In-Jae Yu, Heung-Kyu Lee, Changick Kim
Date: 2021-08-30 00:00:00
Tasks: Image Manipulation, Image Manipulation Detection, Image Manipulation Localization

Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network (CNN) that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network (CAT-Net) that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
Authors: Masanari Kimura, Takuma Nakamura, Yuki Saito
Date: 2021-08-30 00:00:00
Tasks: BIG-bench Machine Learning, set matching

This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images. Although neural networks have been applied to solve this problem, most machine learning-based approaches assume that the training and test data follow the same distribution, which is not always true in real-world scenarios. To address this limitation, we introduce SHIFT15M, a dataset that can be used to evaluate set-to-set matching models when the distribution of data changes between training and testing. We conduct benchmark experiments that demonstrate the performance drop of naive methods due to distribution shift. Additionally, we provide software to handle the SHIFT15M dataset in a simple manner, with the URL for the software to be made available after publication of this manuscript. We believe proposed SHIFT15M dataset provide a valuable resource for evaluating set-to-set matching models under the distribution shift.
Automatic Preprocessing and Ensemble Learning for Low Quality Cell Image Segmentation
Authors: Sota Kato, Kazuhiro Hotta
Date: 2021-08-30 00:00:00
Tasks: Ensemble Learning, Image Segmentation, Segmentation, Semantic Segmentation

We propose an automatic preprocessing and ensemble learning for segmentation of cell images with low quality. It is difficult to capture cells with strong light. Therefore, the microscopic images of cells tend to have low image quality but these images are not good for semantic segmentation. Here we propose a method to translate an input image to the images that are easy to recognize by deep learning. The proposed method consists of two deep neural networks. The first network is the usual training for semantic segmentation, and penultimate feature maps of the first network are used as filters to translate an input image to the images that emphasize each class. This is the automatic preprocessing and translated cell images are easily classified. The input cell image with low quality is translated by the feature maps in the first network, and the translated images are fed into the second network for semantic segmentation. Since the outputs of the second network are multiple segmentation results, we conduct the weighted ensemble of those segmentation images. Two networks are trained by end-to-end manner, and we do not need to prepare images with high quality for the translation. We confirmed that our proposed method can translate cell images with low quality to the images that are easy to segment, and segmentation accuracy has improved using the weighted ensemble learning.
Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video
Authors: Xianhao Tian, Peijia Zheng, Jiwu Huang
Date: 2021-08-30 00:00:00
Tasks: Motion Detection, Moving Object Detection, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking, Privacy Preserving, Semantic Segmentation

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of 4x4 pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.
Enterprise Architecture Model Transformation Engine
Authors: Erik Heiland, Peter Hillmann, Andreas Karcher
Date: 2021-08-15 00:00:00
Tasks: model, Translation

With increasing linkage within value chains, the IT systems of different companies are also being connected with each other. This enables the integration of services within the movement of Industry 4.0 in order to improve the quality and performance of the processes. Enterprise architecture models form the basis for this with a better buisness IT-alignment. However, the heterogeneity of the modeling frameworks and description languages makes a concatenation considerably difficult, especially differences in syntax, semantic and relations. Therefore, this paper presents a transformation engine to convert enterprise architecture models between several languages. We developed the first generic translation approach that is free of specific meta-modeling, which is flexible adaptable to arbitrary modeling languages. The transformation process is defined by various pattern matching techniques using a rule-based description language. It uses set theory and first-order logic for an intuitive description as a basis. The concept is practical evaluated using an example in the area of a large German IT-service provider. Anyhow, the approach is applicable between a wide range of enterprise architecture frameworks.
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
Authors: Mostafa Jahanifar, Neda Zamani Tajeddin, Navid Alemi Koohbanani, Nasir Rajpoot
Date: 2021-08-30 00:00:00
Tasks: Diagnostic, Interactive Segmentation, Segmentation, Semantic Segmentation

From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating different tissue regions manually is a laborious, time-consuming and costly task that requires expert knowledge. On the other hand, the state-of-the-art automatic deep learning models for semantic segmentation require lots of annotated training data and there are only a limited number of tissue region annotated images publicly available. To obviate this issue in computational pathology projects and collect large-scale region annotations efficiently, we propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. The user is only required to draw a simple squiggle inside each region of interest so it will be used as the guiding signal for the model. To deal with the complex appearance and amorph geometry of different tissue regions we introduce several automatic and minimalistic guiding signal generation techniques that help the model to become robust against the variation in the user input. By experimenting on a dataset of breast cancer images, we show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.