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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Amin Ranem Camila Gonzá lez Anirban Mukhopadhyay GRIS Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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Optical See-through Scope for Observing the Global Component of a Scene

Optical See-through Scope for Observing the Global Component...

引用

Virtual Reality and 3D User Interfaces Abstracts and workshops (VRW), ieee conference on

作者： Yoshiaki Makita Daisuke Iwai Kosuke Sato Osaka university

The reflected light on a physical object can be separated into a direct illumination component and a global illumination component. Observing only the global component provides a better understanding of the reflectance property of the surface. In conventional methods, the components are separated using computer vision techniques. In this study, we developed a see-through optical scope that allows a user to selectively observe the global component using high spatial frequency pattern projection and a high spatial frequency mask with a liquid crystal panel. We confirmed that only the global component could be observed with the naked eye through the scope.

关键词： computer vision Visualization Lighting Virtual reality User interfaces Optical imaging Adaptive optics

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computer-aided diagnosis system of lung carcinoma using Convolutional Neural Networks

Computer-aided diagnosis system of lung carcinoma using Conv...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Han, Fangjian Yu, Li Jiang, Yi Lensee Biotechnol Co Ltd Ningbo Peoples R China Cent South Univ Xiangya Hosp 2 Changsha Peoples R China

ISBN: (纸本)9781728193601

With the number of lung cancers' morbidity and mortality is showing a trend of increasing year by year, the demand for pathologists is increasing rapidly, so in this study, we aimed to design a medical pathologically assistant diagnostic system to help pathologists complete diagnostic analysis tasks. A Deep Convolutional Neural Network(DCNN) is adopted to automatically distinguish tumor tissues from normal tissues in digitized hematoxylin and eosin (H&E) stained lung cell pathological slides that collected from The Cancer Genome Atlas (TCGA) and collaborate hospitals, we trained and evaluate WSIs(the whole slide images) captured at 10x magnification and other higher magnification, results show the difference are negligible. Moreover, we also compared the training effect of different models on same level magnification WSIs, the results show that performance of Resnet-18 network model and Resnet-50 network model is nearly consistent. Actually processing time based on Resnet-18 model is shorter than Resnet-50 model, so we don't need deeper network for study. Our system was shown to enormous advantages in accuracy, sensitivity and efficiency, could reduce the burden on pathologists, enable them to spend more time on advanced decision-making tasks, would be widely applied to pathological diagnosis, clinical practice, scientific research and so on.

关键词： Decision making

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The 1st Agriculture-vision Challenge: Methods and Results

The 1st Agriculture-Vision Challenge: Methods and Results

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chiu, Mang Tik Xu, Xingqian Wang, Kai Hobbs, Jennifer Hovakimyan, Naira Huang, Thomas S. Shi, Honghui Wei, Yunchao Huang, Zilong Schwing, Alexander Brunner, Robert Dozier, Ivan Dozier, Wyatt Ghandilyan, Karen Wilson, David Park, Hyunseong Kim, Junhee Kim, Sungho Liu, Qinghui Kampffmeyer, Michael C. Jenssen, Robert Salberg, Arnt B. Barbosa, Alexandre Trevisan, Rodrigo Zhao, Bingchen Yu, Shaozuo Yang, Siwei Wang, Yin Sheng, Hao Chen, Xiao Su, Jingyi Rajagopal, Ram Ng, Andrew Van Thong Huynh Kim, Soo-Hyung Na, In-Seop Baid, Ujjwal Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Tang, Jianyu UIUC Urbana IL 61801 USA Intelinair Indianapolis IN USA Univ Oregon Eugene OR 97403 USA Agcy Def Dev Seoul South Korea DGIST Daegu South Korea Norwegian Comp Ctr Oslo Norway UiT Arctic Univ Norway Oslo Norway Tongji Univ Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chegg Inc Santa Clara CA USA Chonnam Natl Univ Gwangju South Korea Chosun Univ Gwangju South Korea SGGS Inst Engn & Technol Nanded India Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

The first Agriculture-vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task;the link can be found here.

关键词： Semantics Image segmentation pattern recognition conferences Agriculture Computational modeling computer vision

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InstancePose: Fast 6DoF Pose Estimation for Multiple Objects from a Single RGB Image 18

InstancePose: Fast 6DoF Pose Estimation for Multiple Objects...

引用

18th ieee/CVF International conference on computer vision (ICCV)

作者： Aing, Lee Lie, Wen-Nung Chiang, Jui-Chiu Lin, Guo-Shiang Natl Chung Cheng Univ CCU Dept Elect Engn Minxiong Township Chiayi Taiwan Natl Chung Cheng Univ CCU Ctr Innovat Res Aging Soc CIRAS Minxiong Township Chiayi Taiwan Natl Chung Cheng Univ CCU Adv Inst Mfg High Tech Innovat AIM HI Minxiong Township Chiayi Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781665401913

6DoF object pose estimation depends on positional accuracy, implementation complexity and processing speed. This study presents a method to estimate 6DoF object poses for multi-instance object detection that requires less time and is accurate. The proposed method uses a deep neural network, which outputs 4 types of feature maps: the error object mask, semantic object masks, center vector maps (CVM) and 6D coordinate maps. These feature maps are combined in post processing to detect and estimate multi-object 2D-3D correspondences in parallel for PnP RANSAC estimation. The experiments show that the method can process input RGB images containing 7 different object categories/ instances at a speed of 25 frames per second with competitive accuracy, compared with current state-of-the-art methods, which focus only on some specific conditions.

关键词： Deep learning Image segmentation computer vision Three-dimensional displays conferences Semantics Pose estimation

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Cross-dataset Learning for Generalizable Land Use Scene Classification

Cross-dataset Learning for Generalizable Land Use Scene Clas...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dimitri Gominski Valé rie Gouet-Brunet Liming Chen Geography University of Copenhagen Copenhagen Denmark LaSTIG IGN Saint-Mand&#x00E9 France LIRIS Ecole Centrale Lyon Ecully France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or un-seen visual distributions, but are hardly applicable to real-world situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach 1 exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

关键词： Training Visualization computer vision Image analysis conferences Image retrieval Feature extraction

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CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentri...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Zhengwei She, Qi Chalasani, Tejo Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781728193601

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://***/villawang/CatNet.

关键词： computer vision

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Semi-Supervised Learning with Scarce Annotations

Semi-Supervised Learning with Scarce Annotations

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Rebuffi, Sylvestre-Alvise Ehrhardt, Sebastien Han, Kai Vedaldi, Andrea Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9781728193601

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.

关键词： computer vision

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Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Glaucoma Precognition: Recognizing Preclinical Visual Functi...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gupta, Krati Thakur, Anshul Goldbaum, Michael Yousefi, Siamak Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India Univ Calif San Diego Dept Ophthalmol San Diego CA 92103 USA Univ Tennessee Hlth Sci Ctr Dept Ophthalmol Memphis TN 38103 USA Univ Tennessee Hlth Sci Ctr Dept Genet Genom & Informat Memphis TN USA

ISBN: (纸本)9781728193601

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

关键词： vision

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Multiple Transfer Learning and Multi-label Balanced Training Strategies for Facial AU Detection In the Wild

Multiple Transfer Learning and Multi-label Balanced Training...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ji, Sijie Wang, Kai Peng, Xiaojiang Yang, Jianfei Zeng, Zhaoyang Qiao, Yu Nanyang Technol Univ Singapore Singapore Singapore Chinese Acad Sci Shenzhcn Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781728193601

This paper(1) presents SIAT-NTU solution and results of facial action unit (AU) detection in the EmotiNet Challenge 2020. The task aims to detect 23 AUs from facial images in the wild, and its main difficulties lie in the imbalanced AU distribution and discriminative feature learning. We tackle these difficulties from the following aspects. First, to address the unconstrained heterogeneity of in-the-wild images, we detect and align faces with multi-task convolutional neural networks (MTCNN). Second, by using multiple transfer strategies, we pre-train large CNNs on multiple related datasets, e.g. face recognition datasets and facial expression datasets, and fine-tune them on the EmotiNetdataset. Third, we employ a multi-label balanced sampling strategy and a weighted loss to mitigate the imbalance problem. Last but not the least, to further improve performance, we ensemble multiple models and optimize the thresholds for each AU. Our proposed solution achieves an accuracy of 90.13% and F1 of 44.10% in the final test phase. Our Code is available at: https://***/kaiwang960112/enc2020_au_ detection

关键词： Convolutional neural networks

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