scholarly journals Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning

2021 ◽  
Vol 11 (15) ◽  
pp. 6828
Author(s):  
Jeroen M. A. van der Burgt ◽  
Saskia M. Camps ◽  
Maria Antico ◽  
Gustavo Carneiro ◽  
Davide Fontanarosa
Keyword(s):  
Knee Arthroscopy ◽  
3D Ultrasound ◽  
True Positive ◽  
Weak Supervision ◽  
Average Classification Accuracy ◽  
Multiple Image ◽  
Objective Metrics ◽  
Localization Performance ◽  
Weakly Supervised ◽  
Imaging Conditions

This work presents an algorithm based on weak supervision to automatically localize an arthroscope on 3D ultrasound (US). The ultimate goal of this application is to combine 3D US with the 2D arthroscope view during knee arthroscopy, to provide the surgeon with a comprehensive view of the surgical site. The implemented algorithm consisted of a weakly supervised neural network, which was trained on 2D US images of different phantoms mimicking the imaging conditions during knee arthroscopy. Image-based classification was performed and the resulting class activation maps were used to localize the arthroscope. The localization performance was evaluated visually by three expert reviewers and by the calculation of objective metrics. Finally, the algorithm was also tested on a human cadaver knee. The algorithm achieved an average classification accuracy of 88.6% on phantom data and 83.3% on cadaver data. The localization of the arthroscope based on the class activation maps was correct in 92–100% of all true positive classifications for both phantom and cadaver data. These results are relevant because they show feasibility of automatic arthroscope localization in 3D US volumes, which is paramount to combining multiple image modalities that are available during knee arthroscopies.

scholarly journals Weak Supervision Enhanced Generative Network for Question Generation

2019 ◽  
Author(s):  
Yutong Wang ◽  
Jiyuan Zheng ◽  
Qijiong Liu ◽  
Zhou Zhao ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Question Answering ◽  
Interaction Mechanism ◽  
Generation System ◽  
Dialogue System ◽  
Question Generation ◽  
Weak Supervision ◽  
Weakly Supervised ◽  
The Given ◽  
Automatic Question Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

scholarly journals Weakly Supervised Disentanglement by Pairwise Similarities

2020 ◽  
Vol 34 (04) ◽  
pp. 3495-3502 ◽  
Author(s):  
Junxiang Chen ◽  
Kayhan Batmanghelich
Keyword(s):  
Real World ◽  
Latent Variables ◽  
Generative Models ◽  
Experimental Results ◽  
New Method ◽  
Weak Supervision ◽  
Real World Problem ◽  
Variational Autoencoder ◽  
Weakly Supervised

Recently, researches related to unsupervised disentanglement learning with deep generative models have gained substantial popularity. However, without introducing supervision, there is no guarantee that the factors of interest can be successfully recovered (Locatello et al. 2018). Motivated by a real-world problem, we propose a setting where the user introduces weak supervision by providing similarities between instances based on a factor to be disentangled. The similarity is provided as either a binary (yes/no) or real-valued label describing whether a pair of instances are similar or not. We propose a new method for weakly supervised disentanglement of latent variables within the framework of Variational Autoencoder. Experimental results demonstrate that utilizing weak supervision improves the performance of the disentanglement method substantially.

scholarly journals Learning Transferable Self-Attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision

2019 ◽  
Vol 33 ◽  
pp. 9227-9234 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Haichao Shi ◽  
Changsheng Li ◽  
Kai Zheng ◽  
Xiaobin Zhu ◽  
...  
Keyword(s):  
Action Recognition ◽  
Ground Truth ◽  
Classification Performance ◽  
Video Frame ◽  
Weak Supervision ◽  
The Past ◽  
Frame Sequence ◽  
Benchmark Datasets ◽  
Weakly Supervised ◽  
Action Frame

Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short sequences and require ground-truth annotations of each video frame/sequence, which is quite costly and time-consuming. In this paper, given only video-level annotations, we propose a novel weakly supervised framework to simultaneously locate action frames as well as recognize actions in untrimmed videos. Our proposed framework consists of two major components. First, for action frame localization, we take advantage of the self-attention mechanism to weight each frame, such that the influence of background frames can be effectively eliminated. Second, considering that there are trimmed videos publicly available and also they contain useful information to leverage, we present an additional module to transfer the knowledge from trimmed videos for improving the classification performance in untrimmed ones. Extensive experiments are conducted on two benchmark datasets (i.e., THUMOS14 and ActivityNet1.3), and experimental results clearly corroborate the efficacy of our method.

scholarly journals Background Suppression Network for Weakly-Supervised Temporal Action Localization

2020 ◽  
Vol 34 (07) ◽  
pp. 11320-11327 ◽  
Author(s):  
Pilhyeon Lee ◽  
Youngjung Uh ◽  
Hyeran Byun
Keyword(s):  
State Of The Art ◽  
Background Suppression ◽  
Not Given ◽  
Training Strategy ◽  
Training Stage ◽  
Action Localization ◽  
Localization Performance ◽  
Weakly Supervised ◽  
Branch Weight ◽  
Temporal Action

Weakly-supervised temporal action localization is a very challenging problem because frame-wise labels are not given in the training stage while the only hint is video-level labels: whether each video contains action frames of interest. Previous methods aggregate frame-level class scores to produce video-level prediction and learn from video-level action labels. This formulation does not fully model the problem in that background frames are forced to be misclassified as action classes to predict video-level labels accurately. In this paper, we design Background Suppression Network (BaS-Net) which introduces an auxiliary class for background and has a two-branch weight-sharing architecture with an asymmetrical training strategy. This enables BaS-Net to suppress activations from background frames to improve localization performance. Extensive experiments demonstrate the effectiveness of BaS-Net and its superiority over the state-of-the-art methods on the most popular benchmarks – THUMOS'14 and ActivityNet. Our code and the trained model are available at https://github.com/Pilhyeon/BaSNet-pytorch.

scholarly journals Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2032
Author(s):  
Sampo Kuutti ◽  
Richard Bowden ◽  
Saber Fallah
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Model Performance ◽  
Model Parameters ◽  
Safe Driving ◽  
Weak Supervision ◽  
Rule Based ◽  
Learning Agent ◽  
Weakly Supervised

The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone.

scholarly journals Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

2021 ◽  
Vol 13 (24) ◽  
pp. 5009
Author(s):  
Lingbo Huang ◽  
Yushi Chen ◽  
Xin He
Keyword(s):  
Supervised Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
The State ◽  
Weak Supervision ◽  
Training Samples ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

scholarly journals Weakly Supervised Faster-RCNN+FPN to classify small animals in camera trap images

2021 ◽  
Author(s):  
Pierrick Pochelu ◽  
Clara Erard ◽  
Philippe Cordier ◽  
Serge G. Petiton ◽  
Bruno Conche
Keyword(s):  
Camera Trap ◽  
Camera Traps ◽  
Small Animals ◽  
Neural Network Classifier ◽  
High Definition ◽  
Weak Supervision ◽  
Bounding Box ◽  
Multiple Frames ◽  
Weakly Supervised ◽  
Animal Taxon

<div>Camera traps have revolutionized animal research of many species that were previously nearly impossible to observe due to their habitat or behavior.</div><div>Deep learning has the potential to overcome the workload to the class automatically those images according to taxon or empty images. However, a standard deep neural network classifier fails because animals often represent a small portion of the high-definition images. Therefore, we propose a workflow named Weakly Object Detection Faster-RCNN+FPN which suits this challenge. The model is weakly supervised because it requires only the animal taxon label per image but doesn't require any manual bounding box annotations. First, it automatically performs the weakly supervised bounding box annotation using the motion from multiple frames. Then, it trains a Faster-RCNN+FPN model using this weak supervision.<br></div><div>Experimental results have been obtained on two datasets and an easily reproducible testbed.</div>

scholarly journals Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

2020 ◽  
Author(s):  
Tao Shen ◽  
Xiubo Geng ◽  
Guodong Long ◽  
Jing Jiang ◽  
Chengqi Zhang ◽  
...  
Keyword(s):  
Question Answering ◽  
Logical Form ◽  
Computation Time ◽  
Search Space ◽  
Training Data ◽  
Semantic Parsing ◽  
Weak Supervision ◽  
Knowledge Based ◽  
Model Training ◽  
Weakly Supervised

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.

scholarly journals Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

2018 ◽  
Author(s):  
Jason A. Fries ◽  
Paroma Varma ◽  
Vincent S. Chen ◽  
Ke Xiao ◽  
Heliodoro Tejeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Cardiac Mri ◽  
Supervised Machine Learning ◽  
General Strategy ◽  
Weak Supervision ◽  
Validation Experiment ◽  
Mri Sequences ◽  
Weakly Supervised

AbstractBiomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

Investigating Weak Supervision in Deep Ranking

2019 ◽  
Vol 3 (3) ◽  
pp. 155-164 ◽  
Author(s):  
Yukun Zheng ◽  
Yiqun Liu ◽  
Zhen Fan ◽  
Cheng Luo ◽  
Qingyao Ai ◽  
...  
Keyword(s):  
Large Scale ◽  
Document Retrieval ◽  
Exact Matching ◽  
Weak Supervision ◽  
Model Based ◽  
Ranking Models ◽  
Search Engine Result ◽  
Supervised Methods ◽  
Click Model ◽  
Weakly Supervised

Abstract A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies. However, the training processes of these models usually need a large scale of labeled data, leading to data shortage becoming a major hindrance to the improvement of neural ranking models’ performances. Recently, several weakly supervised methods have been proposed to address this challenge with the help of heuristics or users’ interaction in the Search Engine Result Pages (SERPs) to generate weak relevance labels. In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking models. Experimental results show that BM25-based relevance helps models capture more exact matching signals, while click model-based relevance enhances the rankings of documents that may be preferred by users. We further proposed a cascade ranking framework to combine the two weakly supervised relevance, which significantly promotes the ranking performance of neural ranking models and outperforms the best result in the last NTCIR-13 We Want Web (WWW) task. This work reveals the potential of constructing better document retrieval systems based on multiple kinds of weak relevance signals.

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