pwc-archive/papers-with-abstracts huggingface dataset:
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Authors: Liu Li, Qiang Ma, Matthew Sinclair, Antonios Makropoulos, Joseph Hajnal, A. David Edwards, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Date: 2022-05-17 00:00:00
Tasks: Brain Segmentation, Segmentation, Surface Reconstruction

Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of $85.2\%$ for the selected 9 tissue labels.
blob loss: instance imbalance aware loss functions for semantic segmentation
Authors: Florian Kofler, Suprosanna Shit, Ivan Ezhov, Lucas Fidon, Izabela Horvath, Rami Al-Maskari, Hongwei Li, Harsharan Bhatia, Timo Loehr, Marie Piraud, Ali Erturk, Jan Kirschke, Jan C. Peeken, Tom Vercauteren, Claus Zimmer, Benedikt Wiestler, Bjoern Menze
Date: 2022-05-17 00:00:00
Tasks: 3D Semantic Segmentation, Segmentation, Semantic Segmentation

Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory DSC. Nevertheless, detecting tiny instances is crucial for many applications, such as disease monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, \emph{blob loss}, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. \emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters. We extensively evaluate a DSC-based \emph{blob loss} in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5% improvement for MS lesions, 3% improvement for liver tumor, and an average 2% improvement for microscopy segmentation tasks considering F1 score.
CellTypeGraph: A New Geometric Computer Vision Benchmark
Authors: Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht
Date: 2022-05-17 00:00:00
Proceeding: CVPR 2022 1
Tasks: Graph Neural Network, Node Classification

Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem.
Uncertainty-based Network for Few-shot Image Classification
Authors: Minglei Yuan, Qian Xu, Chunhao Cai, Yin-Dong Zheng, Tao Wang, Tong Lu
Date: 2022-05-17 00:00:00
Tasks: Classification, Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification

The transductive inference is an effective technique in the few-shot learning task, where query sets update prototypes to improve themselves. However, these methods optimize the model by considering only the classification scores of the query instances as confidence while ignoring the uncertainty of these classification scores. In this paper, we propose a novel method called Uncertainty-Based Network, which models the uncertainty of classification results with the help of mutual information. Specifically, we first data augment and classify the query instance and calculate the mutual information of these classification scores. Then, mutual information is used as uncertainty to assign weights to classification scores, and the iterative update strategy based on classification scores and uncertainties assigns the optimal weights to query instances in prototype optimization. Extensive results on four benchmarks show that Uncertainty-Based Network achieves comparable performance in classification accuracy compared to state-of-the-art method.
UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry
Authors: Yiming Tu
Date: 2022-05-17 00:00:00
Tasks: Autonomous Driving, Pose Estimation, Scene Flow Estimation

High-precision lidar odomety is an essential part of autonomous driving. In recent years, deep learning methods have been widely used in lidar odomety tasks, but most of the current methods only extract the global features of the point clouds. It is impossible to obtain more detailed point-level features in this way. In addition, only the fully connected layer is used to estimate the pose. The fully connected layer has achieved obvious results in the classification task, but the changes in pose are a continuous rather than discrete process, high-precision pose estimation can not be obtained only by using the fully connected layer. Our method avoids problems mentioned above. We use PointPWC as our backbone network. PointPWC is originally used for scene flow estimation. The scene flow estimation task has a strong correlation with lidar odomety. Traget point clouds can be obtained by adding the scene flow and source point clouds. We can achieve the pose directly through ICP algorithm solved by SVD, and the fully connected layer is no longer used. PointPWC extracts point-level features from point clouds with different sampling levels, which solves the problem of too rough feature extraction. We conduct experiments on KITTI, Ford Campus Vision and Lidar DataSe and Apollo-SouthBay Dataset. Our result is comparable with the state-of-the-art unsupervised deep learing method SelfVoxeLO.
Gender and Racial Bias in Visual Question Answering Datasets
Authors: Yusuke Hirota, Yuta Nakashima, Noa Garcia
Date: 2022-05-17 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Vision-and-language tasks have increasingly drawn more attention as a means to evaluate human-like reasoning in machine learning models. A popular task in the field is visual question answering (VQA), which aims to answer questions about images. However, VQA models have been shown to exploit language bias by learning the statistical correlations between questions and answers without looking into the image content: e.g., questions about the color of a banana are answered with yellow, even if the banana in the image is green. If societal bias (e.g., sexism, racism, ableism, etc.) is present in the training data, this problem may be causing VQA models to learn harmful stereotypes. For this reason, we investigate gender and racial bias in five VQA datasets. In our analysis, we find that the distribution of answers is highly different between questions about women and men, as well as the existence of detrimental gender-stereotypical samples. Likewise, we identify that specific race-related attributes are underrepresented, whereas potentially discriminatory samples appear in the analyzed datasets. Our findings suggest that there are dangers associated to using VQA datasets without considering and dealing with the potentially harmful stereotypes. We conclude the paper by proposing solutions to alleviate the problem before, during, and after the dataset collection process.
Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
Authors: Yu Wang, Binbin Zhu, Lingsi Kong, Jianlin Wang, Bin Gao, Jianhua Wang, Dingcheng Tian, YuDong Yao
Date: 2022-05-17 00:00:00
Tasks: Segmentation

Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can observe the target nerve and its surrounding structures, the puncture needle's advancement, and local anesthetics spread in real-time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently. Here, we establish a public dataset containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produce the BP segmentation ground truth and label brachial plexus trunks. We design a brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluate BPSegSys' performance in terms of intersection-over-union (IoU), a commonly used performance measure for segmentation experiments. Considering three dataset groups in our established public dataset, the IoU of BPSegSys are 0.5238, 0.4715, and 0.5029, respectively, which exceed the IoU 0.5205, 0.4704, and 0.4979 of experienced doctors. In addition, we show that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value.
Using artificial intelligence to detect chest X-rays with no significant findings in a primary health care setting in Oulu, Finland
Authors: Tommi Keski-Filppula, Marko Nikki, Marianne Haapea, Naglis Ramanauskas, Osmo Tervonen
Date: 2022-05-17 00:00:00
Tasks: Specificity

Objectives: To assess the use of artificial intelligence-based software in ruling out chest X-ray cases, with no significant findings in a primary health care setting. Methods: In this retrospective study, a commercially available artificial intelligence (AI) software was used to analyse 10 000 chest X-rays of Finnish primary health care patients. In studies with a mismatch between an AI normal report and the original radiologist report, a consensus read by two board-certified radiologists was conducted to make the final diagnosis. Results: After the exclusion of cases not meeting the study criteria, 9579 cases were analysed by AI. Of these cases, 4451 were considered normal in the original radiologist report and 4644 after the consensus reading. The number of cases correctly found nonsignificant by AI was 1692 (17.7% of all studies and 36.4% of studies with no significant findings). After the consensus read, there were nine confirmed false-negative studies. These studies included four cases of slightly enlarged heart size, four cases of slightly increased pulmonary opacification and one case with a small unilateral pleural effusion. This gives the AI a sensitivity of 99.8% (95% CI= 99.65-99.92) and specificity of 36.4 % (95% CI= 35.05-37.84) for recognising significant pathology on a chest X-ray. Conclusions: AI was able to correctly rule out 36.4% of chest X-rays with no significant findings of primary health care patients, with a minimal number of false negatives that would lead to effectively no compromise on patient safety. No critical findings were missed by the software.
Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients
Authors: Chia-Hung Yang, Yun-Chien Cheng, Chin Kuo
Date: 2022-05-17 00:00:00
Tasks: Generative Adversarial Network, Image Generation

The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically for pulmonary embolism diagnoses. Nowadays, CTPA images are the gold standard computerized detection method to determine and identify the symptoms of pulmonary embolism (PE), although performing CTPA is harmful for patients and also expensive. Therefore, we aim to detect possible PE patients through CT images. The system will simulate CTPA images with deep learning models for the identification of PE patients' symptoms, providing physicians with another reference for determining PE patients. In this study, the simulated CTPA image generation system uses a generative antagonistic network to enhance the features of pulmonary vessels in the CT images to strengthen the reference value of the images and provide a basis for hospitals to judge PE patients. We used the CT images of 22 patients from National Cheng Kung University Hospital and the corresponding CTPA images as the training data for the task of simulating CTPA images and generated them using two sets of generative countermeasure networks. This study is expected to propose a new approach to the clinical diagnosis of pulmonary embolism, in which a deep learning network is used to assist in the complex screening process and to review the generated simulated CTPA images, allowing physicians to assess whether a patient needs to undergo detailed testing for CTPA, improving the speed of detection of pulmonary embolism and significantly reducing the number of undetected patients.
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey
Authors: Paul Wimmer, Jens Mehnert, Alexandru Paul Condurache
Date: 2022-05-17 00:00:00
Tasks: Model Compression, Survey

State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costly. A big part of these costs is caused by training the network. Model compression lowers storage and transfer costs, and can further make training more efficient by decreasing the number of computations in the forward and/or backward pass. Thus, compressing networks also at training time while maintaining a high performance is an important research topic. This work is a survey on methods which reduce the number of trained weights in deep learning models throughout the training. Most of the introduced methods set network parameters to zero which is called pruning. The presented pruning approaches are categorized into pruning at initialization, lottery tickets and dynamic sparse training. Moreover, we discuss methods that freeze parts of a network at its random initialization. By freezing weights, the number of trainable parameters is shrunken which reduces gradient computations and the dimensionality of the model's optimization space. In this survey we first propose dimensionality reduced training as an underlying mathematical model that covers pruning and freezing during training. Afterwards, we present and discuss different dimensionality reduced training methods.
MATrIX -- Modality-Aware Transformer for Information eXtraction
Authors: Thomas Delteil, Edouard Belval, Lei Chen, Luis Goncalves, Vijay Mahadevan
Date: 2022-05-17 00:00:00
Tasks: document understanding

We present MATrIX - a Modality-Aware Transformer for Information eXtraction in the Visual Document Understanding (VDU) domain. VDU covers information extraction from visually rich documents such as forms, invoices, receipts, tables, graphs, presentations, or advertisements. In these, text semantics and visual information supplement each other to provide a global understanding of the document. MATrIX is pre-trained in an unsupervised way with specifically designed tasks that require the use of multi-modal information (spatial, visual, or textual). We consider the spatial and text modalities all at once in a single token set. To make the attention more flexible, we use a learned modality-aware relative bias in the attention mechanism to modulate the attention between the tokens of different modalities. We evaluate MATrIX on 3 different datasets each with strong baselines.
A Linear Comb Filter for Event Flicker Removal
Authors: Ziwei Wang, Dingran Yuan, Yonhon Ng, Robert Mahony
Date: 2022-05-17 00:00:00

Event cameras are bio-inspired sensors that capture per-pixel asynchronous intensity change rather than the synchronous absolute intensity frames captured by a classical camera sensor. Such cameras are ideal for robotics applications since they have high temporal resolution, high dynamic range and low latency. However, due to their high temporal resolution, event cameras are particularly sensitive to flicker such as from fluorescent or LED lights. During every cycle from bright to dark, pixels that image a flickering light source generate many events that provide little or no useful information for a robot, swamping the useful data in the scene. In this paper, we propose a novel linear filter to preprocess event data to remove unwanted flicker events from an event stream. The proposed algorithm achieves over 4.6 times relative improvement in the signal-to-noise ratio when compared to the raw event stream due to the effective removal of flicker from fluorescent lighting. Thus, it is ideally suited to robotics applications that operate in indoor settings or scenes illuminated by flickering light sources.
Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder
Authors: Sabir Hossain, Xianke Lin
Date: 2022-05-17 00:00:00
Tasks: Depth Estimation, Stereo Depth Estimation

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to use different depth estimators to obtain pseudo point clouds like LiDAR to obtain better performance. Moreover, the training and validating strategy of the depth estimator has adopted stereo imagery data to estimate more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms on KITTI benchmark while yielding point clouds significantly faster than other approaches.
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Authors: Hexin Dong, ZiFan Chen, Mingze Yuan, Yutong Xie, Jie Zhao, Fei Yu, Bin Dong, Li Zhang
Date: 2022-05-17 00:00:00
Tasks: Anomaly Segmentation, Few-Shot Learning, Metric Learning, Segmentation, Semantic Segmentation

As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution (OOD) objects, especially under a few-shot condition. The current state-of-the-art (SOTA) method, Deep Metric Learning Network (DMLNet), relies on pixel-level metric learning, with which the identification of similar regions having different semantics is difficult. Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning. RAML improves the integrity of the segmented anomaly regions. Moreover, we propose a novel meta-channel aggregation (MCA) module to further separate anomaly regions, forming high-quality sub-region candidates and thereby improving the model performance for OOD objects. To evaluate the proposed RAML, we have conducted extensive experiments and ablation studies on Lost And Found and Road Anomaly datasets for anomaly segmentation and the CityScapes dataset for incremental few-shot learning. The results show that the proposed RAML achieves SOTA performance in both stages of open world segmentation. Our code and appendix are available at https://github.com/czifan/RAML.
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Authors: Arda Sahiner, Tolga Ergen, Batu Ozturkler, John Pauly, Morteza Mardani, Mert Pilanci
Date: 2022-05-17 00:00:00
Tasks: Inductive Bias

Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of attention is not well understood. To address this issue, this paper analyzes attention through the lens of convex duality. For the non-linear dot-product self-attention, and alternative mechanisms such as MLP-mixer and Fourier Neural Operator (FNO), we derive equivalent finite-dimensional convex problems that are interpretable and solvable to global optimality. The convex programs lead to {\it block nuclear-norm regularization} that promotes low rank in the latent feature and token dimensions. In particular, we show how self-attention networks implicitly clusters the tokens, based on their latent similarity. We conduct experiments for transferring a pre-trained transformer backbone for CIFAR-100 classification by fine-tuning a variety of convex attention heads. The results indicate the merits of the bias induced by attention compared with the existing MLP or linear heads.
Collaborative Attention Memory Network for Video Object Segmentation
Authors: Zhixing Huang, Junli Zha, Fei Xie, Yuwei Zheng, Yuandong Zhong, Jinpeng Tang
Date: 2022-05-17 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Semi-supervised video object segmentation is a fundamental yet Challenging task in computer vision. Embedding matching based CFBI series networks have achieved promising results by foreground-background integration approach. Despite its superior performance, these works exhibit distinct shortcomings, especially the false predictions caused by little appearance instances in first frame, even they could easily be recognized by previous frame. Moreover, they suffer from object's occlusion and error drifts. In order to overcome the shortcomings , we propose Collaborative Attention Memory Network with an enhanced segmentation head. We introduce a object context scheme that explicitly enhances the object information, which aims at only gathering the pixels that belong to the same category as a given pixel as its context. Additionally, a segmentation head with Feature Pyramid Attention(FPA) module is adopted to perform spatial pyramid attention structure on high-level output. Furthermore, we propose an ensemble network to combine STM network with all these new refined CFBI network. Finally, we evaluated our approach on the 2021 Youtube-VOS challenge where we obtain 6th place with an overall score of 83.5\%.