pwc-archive/papers-with-abstracts huggingface dataset:
Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
Authors: Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica
Date: 2020-11-23 00:00:00
Tasks: Anatomy, Lesion Segmentation, Transfer Learning

We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by the proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16, which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely fluid-attenuated inversion recovery (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license, and this paper complies with the Machine learning reproducibility checklist. By implementing practical transfer learning for 3D data representation, we could segment heavily unbalanced data without selective sampling and achieved more reliable results using less training data in a single modality. From a medical perspective, the unimodal approach gives an advantage in real praxis as it does not require co-registration nor additional scanning time during an examination. Although modern medical imaging methods capture high-resolution 3D anatomy scans suitable for computer-aided detection system processing, deployment of automatic systems for interpretation of radiology imaging is still rather theoretical in many medical areas. Our work aims to bridge the gap by offering a solution for partial research questions.
Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks
Authors: Martin Kolarik, Radim Burget, Kamil Riha
Date: 2020-11-23 00:00:00
Tasks: Segmentation

The widespread use of Batch Normalization has enabled training deeper neural networks with more stable and faster results. However, the Batch Normalization works best using large batch size during training and as the state-of-the-art segmentation convolutional neural network architectures are very memory demanding, large batch size is often impossible to achieve on current hardware. We evaluate the alternative normalization methods proposed to solve this issue on a problem of binary spine segmentation from 3D CT scan. Our results show the effectiveness of Instance Normalization in the limited batch size neural network training environment. Out of all the compared methods the Instance Normalization achieved the highest result with Dice coefficient = 0.96 which is comparable to our previous results achieved by deeper network with longer training time. We also show that the Instance Normalization implementation used in this experiment is computational time efficient when compared to the network without any normalization method.
A Closed-Form Solution to Local Non-Rigid Structure-from-Motion
Authors: Shaifali Parashar, Yuxuan Long, Mathieu Salzmann, Pascal Fua
Date: 2020-11-23 00:00:00
Tasks: Form

A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations. The systems of equations derived in previous work, however, are of high degree, having up to five real solutions, thus requiring a computationally expensive strategy to select a unique solution. Furthermore, they suffer from degeneracies that make the resulting estimates unreliable, without any mechanism to identify this situation. In this paper, we show that, under widely applicable assumptions, we can derive a new system of equation in terms of the surface normals whose two solutions can be obtained in closed-form and can easily be disambiguated locally. Our formalism further allows us to assess how reliable the estimated local normals are and, hence, to discard them if they are not. Our experiments show that our reconstructions, obtained from two or more views, are significantly more accurate than those of state-of-the-art methods, while also being faster.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
Authors: Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang, Ye Wang, Pradeep Kumar Jayaraman, Krishna Murthy Jatavallabhula
Date: 2020-11-23 00:00:00
Tasks: 3D Point Cloud Classification, Benchmarking, Point Cloud Classification, Point cloud classification dataset, Robust classification

The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have largely remained the same. Most datasets comprise clean, clutter-free pointclouds canonicalized for pose. Models trained on these datasets fail in uninterpretible and unintuitive ways when presented with data that contains transformations "unseen" at train time. While data augmentation enables models to be robust to "previously seen" input transformations, 1) we show that this does not work for unseen transformations during inference, and 2) data augmentation makes it difficult to analyze a model's inherent robustness to transformations. To this end, we create a publicly available dataset for robustness analysis of point cloud classification models (independent of data augmentation) to input transformations, called RobustPointSet. Our experiments indicate that despite all the progress in the point cloud classification, there is no single architecture that consistently performs better -- several fail drastically -- when evaluated on transformed test sets. We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation. RobustPointSet can be accessed through https://github.com/AutodeskAILab/RobustPointSet.
Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement
Authors: Angel Villar-Corrales, Veniamin I. Morgenshtern
Date: 2020-11-23 00:00:00
Tasks: Clustering, Deep Clustering, Image Clustering

In the last few years, large improvements in image clustering have been driven by the recent advances in deep learning. However, due to the architectural complexity of deep neural networks, there is no mathematical theory that explains the success of deep clustering techniques. In this work we introduce Projected-Scattering Spectral Clustering (PSSC), a state-of-the-art, stable, and fast algorithm for image clustering, which is also mathematically interpretable. PSSC includes a novel method to exploit the geometric structure of the scattering transform of small images. This method is inspired by the observation that, in the scattering transform domain, the subspaces formed by the eigenvectors corresponding to the few largest eigenvalues of the data matrices of individual classes are nearly shared among different classes. Therefore, projecting out those shared subspaces reduces the intra-class variability, substantially increasing the clustering performance. We call this method Projection onto Orthogonal Complement (POC). Our experiments demonstrate that PSSC obtains the best results among all shallow clustering algorithms. Moreover, it achieves comparable clustering performance to that of recent state-of-the-art clustering techniques, while reducing the execution time by more than one order of magnitude. In the spirit of reproducible research, we publish a high quality code repository along with the paper.
Abiotic Stress Prediction from RGB-T Images of Banana Plantlets
Authors: Sagi Levanon, Oshry Markovich, Itamar Gozlan, Ortal Bakhshian, Alon Zvirin, Yaron Honen, Ron Kimmel
Date: 2020-11-23 00:00:00
Tasks: Prediction

Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields. Multi-modal data, acquired from a variety of sensors, offers diverse perspectives and is expected to benefit the prediction process. We present several methods and strategies for abiotic stress prediction in banana plantlets, on a dataset acquired during a two and a half weeks period, of plantlets subject to four separate water and fertilizer treatments. The dataset consists of RGB and thermal images, taken once daily of each plant. Results are encouraging, in the sense that neural networks exhibit high prediction rates (over $90\%$ amongst four classes), in cases where there are hardly any noticeable features distinguishing the treatments, much higher than field experts can supply.
High Fidelity Interactive Video Segmentation Using Tensor Decomposition Boundary Loss Convolutional Tessellations and Context Aware Skip Connections
Authors: Anthony D. Rhodes, Manan Goel
Date: 2020-11-23 00:00:00
Tasks: Interactive Segmentation, Segmentation, Tensor Decomposition, Video Segmentation, Video Semantic Segmentation

We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a convolutional network with context-aware skip connections, and compressed, hypercolumn image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) We use a statistically-principled tensor decomposition procedure to modulate the number of hypercolumn features and (2) We render these features in their native resolution using a convolutional tessellation technique. For improved pixel level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including greenscreen and various composited scenes with corresponding, hand crafted, pixel level segmentations. Our work presents an extension to improvement to state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.
Transfer Learning for Oral Cancer Detection using Microscopic Images
Authors: Rutwik Palaskar, Renu Vyas, Vilas Khedekar, Sangeeta Palaskar, Pranjal Sahu
Date: 2020-11-23 00:00:00
Tasks: Data Augmentation, Transfer Learning

Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare numerous state-of-the-art models via transfer learning approach and collect and release an augmented dataset of high-quality microscopic images of oral cancer. We present a comprehensive study of different models and report their performance on this type of data. Overall, we obtain a 10-15% absolute improvement with transfer learning methods compared to a simple Convolutional Neural Network baseline. Ablation studies show the added benefit of data augmentation techniques with finetuning for this task.
Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
Authors: J. de Curtó, R. Duvall
Date: 2020-11-23 00:00:00
Tasks: Style Transfer

Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang'E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
Authors: Hala Lamdouar, Charig Yang, Weidi Xie, Andrew Zisserman
Date: 2020-11-23 00:00:00
Tasks: Motion Segmentation, Object, Object Discovery, Segmentation, Semantic Segmentation

The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while maintaining the object permanence even when motion is absent at some point. (ii) We collect the first large-scale Moving Camouflaged Animals (MoCA) video dataset, which consists of over 140 clips across a diverse range of animals (67 categories). (iii) We demonstrate the effectiveness of the proposed model on MoCA, and achieve competitive performance on the unsupervised segmentation protocol on DAVIS2016 by only relying on motion.
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression
Authors: Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao
Date: 2020-11-18 00:00:00

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. When training a DNN model, the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks. Different from the state-of-the-art solutions that adopt image-based lossy compressors such as JPEG to compress the activation data, our framework purposely designs error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we provide theoretical analysis on the compression error propagation from the altered activation data to the gradients, and then empirically investigate the impact of altered gradients over the entire training process. Based on these analyses, we then propose an improved lossy compressor and an adaptive scheme to dynamically configure the lossy compression error-bound and adjust the training batch size to further utilize the saved memory space for additional speedup. We evaluate our design against state-of-the-art solutions with four popular DNNs and the ImageNet dataset. Results demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5x and 1.8x over the baseline training and state-of-the-art framework with compression, respectively, with little or no accuracy loss.
Semantic SLAM with Autonomous Object-Level Data Association
Authors: Zhentian Qian, Kartik Patath, Jie Fu, Jing Xiao
Date: 2020-11-20 00:00:00
Tasks: Object, Semantic SLAM, Simultaneous Localization and Mapping, Task Planning

It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and environments to be navigated. While semantic SLAM has gained increasing attention, there is little research on semanticlevel data association based on semantic objects, i.e., object-level data association. In this paper, we propose a novel object-level data association algorithm based on bag of words algorithm, formulated as a maximum weighted bipartite matching problem. With object-level data association solved, we develop a quadratic-programming-based semantic object initialization scheme using dual quadric and introduce additional constraints to improve the success rate of object initialization. The integrated semantic-level SLAM system can achieve high-accuracy object-level data association and real-time semantic mapping as demonstrated in the experiments. The online semantic map building and semantic-level localization capabilities facilitate semantic-level mapping and task planning in a priori unknown environment.
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
Authors: Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck, Andreas Nürnberger
Date: 2020-11-20 00:00:00
Tasks: Liver Segmentation, Segmentation, SSIM

Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth. A possible solution for the problem would be an unsupervised deep learning based approach for automated segmentation, which this research work tries to address. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. In addition, to suppress noise in the segmentation we added attention gates to the skip connections. The loss for the segmentation output was calculated using soft N-Cuts and for the reconstruction output using SSIM. Conditional Random Fields were used as a post-processing step to fine-tune the results. The proposed method has shown promising results, with a dice coefficient of 0.88 for the liver segmentation compared against manual segmentation.
Backdoor Attacks on the DNN Interpretation System
Authors: Shihong Fang, Anna Choromanska
Date: 2020-11-21 00:00:00
Tasks: Backdoor Attack

Interpretability is crucial to understand the inner workings of deep neural networks (DNNs) and many interpretation methods generate saliency maps that highlight parts of the input image that contribute the most to the prediction made by the DNN. In this paper we design a backdoor attack that alters the saliency map produced by the network for an input image only with injected trigger that is invisible to the naked eye while maintaining the prediction accuracy. The attack relies on injecting poisoned data with a trigger into the training data set. The saliency maps are incorporated in the penalty term of the objective function that is used to train a deep model and its influence on model training is conditioned upon the presence of a trigger. We design two types of attacks: targeted attack that enforces a specific modification of the saliency map and untargeted attack when the importance scores of the top pixels from the original saliency map are significantly reduced. We perform empirical evaluation of the proposed backdoor attacks on gradient-based and gradient-free interpretation methods for a variety of deep learning architectures. We show that our attacks constitute a serious security threat when deploying deep learning models developed by untrusty sources. Finally, in the Supplement we demonstrate that the proposed methodology can be used in an inverted setting, where the correct saliency map can be obtained only in the presence of a trigger (key), effectively making the interpretation system available only to selected users.
MRI-Guided High Intensity Focused Ultrasound of Liver and Kidney
Authors: Baudouin Denis de Senneville, Mario Ries, Wilbert Bartels, Chrit Moonen
Date: 2020-11-21 00:00:00
Tasks: Motion Compensation, Vocal Bursts Intensity Prediction

High Intensity Focused Ultrasound (HIFU) can be used to achieve a local temperature increase deep inside the human body in a non-invasive way. MRI guidance of the procedure allows in situ target definition. In addition, MRI can be used to provide continuous temperature mapping during HIFU for spatial and temporal control of the heating procedure and prediction of the final lesion based on the received thermal dose. Temperature mapping of mobile organs as kidney and liver is challenging, as well as real-time processing methods for feedback control of the HIFU procedure. In this paper, recent technological advances are reviewed in MR temperature mapping of these organs, in motion compensation of the HIFU beam, in intercostal HIFU sonication, and in volumetric ablation and feedback control strategies. Recent pre-clinical studies have demonstrated the feasibility of each of these novel methods. The perspectives to translate those advances into the clinic are addressed. It can be concluded that MR guided HIFU for ablation in liver and kidney appears feasible but requires further work on integration of technologically advanced methods.
A System for Automatic Rice Disease Detection from Rice Paddy Images Serviced via a Chatbot
Authors: Pitchayagan Temniranrat, Kantip Kiratiratanapruk, Apichon Kitvimonrat, Wasin Sinthupinyo, Sujin Patarapuwadol
Date: 2020-11-21 00:00:00
Tasks: Chatbot, object-detection, Object Detection

A LINE Bot System to diagnose rice diseases from actual paddy field images was developed and presented in this paper. It was easy-to-use and automatic system designed to help rice farmers improve the rice yield and quality. The targeted images were taken from the actual paddy environment without special sample preparation. We used a deep learning neural networks technique to detect rice diseases from the images. We developed an object detection model training and refinement process to improve the performance of our previous research on rice leave diseases detection. The process was based on analyzing the model's predictive results and could be repeatedly used to improve the quality of the database in the next training of the model. The deployment model for our LINE Bot system was created from the selected best performance technique in our previous paper, YOLOv3, trained by refined training data set. The performance of the deployment model was measured on 5 target classes and found that the Average True Positive Point improved from 91.1% in the previous paper to 95.6% in this study. Therefore, we used this deployment model for Rice Disease LINE Bot system. Our system worked automatically real-time to suggest primary diagnosis results to the users in the LINE group, which included rice farmers and rice disease specialists. They could communicate freely via chat. In the real LINE Bot deployment, the model's performance was measured by our own defined measurement Average True Positive Point and was found to be an average of 78.86%. The system was fast and took only 2-3 s for detection process in our system server.