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Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis

Key Patches Are All You Need: A Multiple Instance Learning F...

引用

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

作者： D. J. Araújo M. R. Verdelho A. Bissoto J. C. Nascimento C. Santiago C. Barata LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal Institute of Computing Recod.ai Lab University of Campinas Brazil Lisbon ELLIS Unit

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning models have revolutionized the field of medical image analysis, due to their outstanding performances. However, they are sensitive to spurious correlations, often taking advantage of dataset bias to improve results for in-domain data, but jeopardizing their generalization capabilities. In this paper, we propose to limit the amount of information these models use to reach the final classification, by using a multiple instance learning (MIL) framework. MIL forces the model to use only a (small) subset of patches in the image, identifying discriminative regions. This mimics the clinical procedures, where medical decisions are based on localized findings. We evaluate our framework on two medical applications: skin cancer diagnosis using dermoscopy and breast cancer diagnosis using mammography. Our results show that using only a subset of the patches does not compromise diagnostic performance for in-domain data, compared to the baseline approaches. However, our approach is more robust to shifts in patient demographics, while also providing more detailed explanations about which regions contributed to the decision. Code is available at: https://***/diogojpa99/Medical-Multiple-Instance-Learning.

关键词： Image analysis Pipelines MIMICs Focusing Breast cancer pattern recognition Mirrors

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DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

DatasetGAN: Efficient Labeled Data Factory with Minimal Huma...

引用

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

作者： Zhang, Yuxuan Ling, Huan Gao, Jun Yin, Kangxue Lafleche, Jean-Francois Barriuso, Adela Torralba, Antonio Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada MIT Cambridge MA 02139 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665445092

We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time consuming to annotate. Our method relies on the power of recent GANs to generate realistic images. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. Training the decoder only needs a few labeled examples to generalize to the rest of the latent space, resulting in an infinite annotated dataset generator! These generated datasets can then be used for training any computer vision architecture just as real datasets are. As only a few images need to be manually segmented, it becomes possible to annotate images in extreme detail and generate datasets with rich object and part segmentations. To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts. Our approach outperforms all semi-supervised baselines significantly and is on par with fully supervised methods, which in some cases require as much as 100x more annotated data as our method.

关键词： Training Image segmentation computer vision Semantics computer architecture Semisupervised learning Generative adversarial networks

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Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

Instant-Teaching: An End-to-End Semi-Supervised Object Detec...

引用

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

作者： Zhou, Qiang Yu, Chaohui Wang, Zhibin Qian, Qi Li, Hao Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Supervised learning based object detection frameworks demand plenty of laborious manual annotations, which may not be practical in real applications. Semi-supervised object detection (SSOD) can effectively leverage unlabeled data to improve the model performance, which is of great significance for the application of object detection models. In this paper, we revisit SSOD and propose Instant-Teaching, a completely end-to-end and effective SSOD framework, which uses instant pseudo labeling with extended weak-strong data augmentations for teaching during each training iteration. To alleviate the confirmation bias problem and improve the quality of pseudo annotations, we further propose a co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching* . Extensive experiments on both MS-COCO and PASCAL VOC datasets substantiate the superiority of our framework. Specifically, our method surpasses state-of-the-art methods by 4.2 mAP on MS-COCO when using 2% labeled data. Even with full supervised information of MS-COCO, the proposed method still outperforms state-of-the-art methods by about 1.0 mAP. On PASCAL VOC, we can achieve more than 5 mAP improvement by applying VOC07 as labeled data and VOC12 as unlabeled data.

关键词： Training computer vision Annotations Supervised learning Object detection Detectors Manuals

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Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation

Railroad is not a Train: Saliency as Pseudo-pixel Supervisio...

引用

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

作者： Lee, Seungho Lee, Minhyun Lee, Jongwuk Shim, Hyunjung Yonsei Univ Seoul South Korea Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, namely Explicit Pseudo-pixel Supervision (EPS), which learns from pixel-level feedback by combining two weak supervisions;the image-level label provides the object identity via the localization map and the saliency map from the off-the-shelf saliency detection model offers rich boundaries. We devise a joint training strategy to fully utilize the complementary relationship between both information. Our method can obtain accurate object boundaries and discard co-occurring pixels, thereby significantly improving the quality of pseudo-masks. Experimental results show that the proposed method remarkably outperforms existing methods by resolving key challenges of WSSS and achieves the new state-of-the-art performance on both PASCAL VOC 2012 and MS COCO 2014 datasets. The code is available at https://***/halbielee/EPS.

关键词： Location awareness Training Image segmentation computer vision Codes Semantics pattern recognition

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End-to-End Rotation Averaging with Multi-Source Propagation

End-to-End Rotation Averaging with Multi-Source Propagation

引用

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

作者： Yang, Luwei Li, Heng Rahim, Jamal Ahmed Cui, Zhaopeng Tan, Ping Simon Fraser Univ Burnaby BC Canada Zhejiang Univ State Key Lab CAD&CG Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

This paper presents an end-to-end neural network for multiple rotation averaging in SfM. Due to the manifold constraint of rotations, conventional methods usually take two separate steps involving spanning tree based initialization and iterative nonlinear optimization respectively. These methods can suffer from bad initializations due to the noisy spanning tree or outliers in input relative rotations. To handle these problems, we propose to integrate initialization and optimization together in an unified graph neural network via a novel differentiable multi-source propagation module. Specifically, our network utilizes the image context and geometric cues in feature correspondences to reduce the impact of outliers. Furthermore, unlike the methods that utilize the spanning tree to initialize orientations according to a single reference node in a top-down manner, our network initializes orientations according to multiple sources while utilizing information from all neighbors in a differentiable way. More importantly, our end-to-end formulation also enables iterative re-weighting of input relative orientations at test time to improve the accuracy of the final estimation by minimizing the impact of outliers. We demonstrate the effectiveness of our method on two real-world datasets, achieving state-of-the-art performance.

关键词： Manifolds Image edge detection Estimation Graph neural networks pattern recognition Internet Reliability

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Self-Supervised 3D Mesh Reconstruction from Single Images

Self-Supervised 3D Mesh Reconstruction from Single Images

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

作者： Hu, Tao Wang, Liwei Xu, Xiaogang Liu, Shu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation. However, without explicit 3D attribute-level supervision, it is still difficult to achieve satisfying reconstruction accuracy. In this paper, we propose a Self-supervised Mesh Reconstruction (SMR) approach to enhance 3D mesh attribute learning process. Our approach is motivated by observations that (1) 3D attributes from interpolation and prediction should be consistent, and (2) feature representation of landmarks from all images should be consistent. By only requiring silhouette mask annotation, our SMR can be trained in an end-to-end manner and generalizes to reconstruct natural objects of birds, cows, motorbikes, etc. Experiments demonstrate that our approach improves both 2D supervised and unsupervised 3D mesh reconstruction on multiple datasets. We also show that our model can be adapted to other image synthesis tasks, e.g., novel view generation, shape transfer, and texture transfer, with promising results.

关键词： Solid modeling Three-dimensional displays Annotations Shape Motorcycles Reconstruction algorithms pattern recognition

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Boundary IoU: Improving Object-Centric Image Segmentation Evaluation

Boundary IoU: Improving Object-Centric Image Segmentation Ev...

引用

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

作者： Cheng, Bowen Girshick, Ross Dollar, Piotr Berg, Alexander C. Kirillov, Alexander UIUC Urbana IL 61820 USA Facebook AI Res FAIR Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects. The new quality measure displays several desirable characteristics like symmetry w. r. t. prediction/ground truth pairs and balanced responsiveness across scales, which makes it more suitable for segmentation evaluation than other boundary-focused measures like Trimap IoU and F-measure. Based on Boundary IoU, we update the standard evaluation protocols for instance and panoptic segmentation tasks by proposing the Boundary AP (Average Precision) and Boundary PQ (Panoptic Quality) metrics, respectively. Our experiments show that the new evaluation metrics track boundary quality improvements that are generally overlooked by current Mask Jo U-based evaluation metrics. We hope that the adoption of the new boundary-sensitive evaluation metrics will lead to rapid progress in segmentation methods that improve boundary quality.

关键词： Image segmentation Protocols Measurement uncertainty Tools Size measurement Performance analysis pattern recognition

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Contrastive Embedding for Generalized Zero-Shot Learning

Contrastive Embedding for Generalized Zero-Shot Learning

引用

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

作者： Han, Zongyan Fu, Zhenyong Chen, Shuo Yang, Jian Nanjing Univ Sci & Technol PCALab Nanjing Peoples R China RIKEN Ctr Adv Intelligence Project Wako Saitama Japan

ISBN: (纸本)9781665445092

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL classification since it lacks discriminative information. To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework. The hybrid GZSL approach maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a contrastive embedding (CE) for our hybrid GZSL framework. The proposed contrastive embedding can leverage not only the class-wise supervision but also the instance-wise supervision, where the latter is usually neglected by existing GZSL researches. We evaluate our proposed hybrid GZSL framework with contrastive embedding, named CE-GZSL, on five benchmark datasets. The results show that our CE-GZSL method can outperform the state-of-the-arts by a significant margin on three datasets.

关键词： Visualization computer vision Codes Benchmark testing Hybrid power systems pattern recognition

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Divergence Optimization for Noisy Universal Domain Adaptation

Divergence Optimization for Noisy Universal Domain Adaptatio...

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

作者： Yu, Qing Hashimoto, Atsushi Ushiku, Yoshitaka Univ Tokyo Tokyo Japan OMRON SINIC X Corp Tokyo Japan

ISBN: (纸本)9781665445092

Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label sets. In practice, however, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain with limited resources. Existing UniDA methods rely on source samples with correct annotations, which greatly limits their application in the real world. Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain. This paper introduces a two-head convolutional neural network framework to solve all problems simultaneously. Our network consists of one common feature generator and two classifiers with different decision boundaries. By optimizing the divergence between the two classifiers' outputs, we can detect noisy source samples, find "unknown" classes in the target domain, and align the distribution of the source and target domains. In an extensive evaluation of different domain adaptation settings, the proposed method outperformed existing methods by a large margin in most settings.

关键词： computer vision Adaptation models Annotations Computational modeling Benchmark testing Generators pattern recognition

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Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing

Exploring Heterogeneous Clues for Weakly-Supervised Audio-Vi...

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

作者： Wu, Yu Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

We investigate the weakly-supervised audio-visual video parsing task, which aims to parse a video into temporal event segments and predict the audible or visible event categories. The task is challenging since there only exist video-level event labels for training, without indicating the temporal boundaries and modalities. Previous works take the overall event labels to supervise both audio and visual model predictions. However, we argue that such overall labels harm the model training due to the audio-visual asynchrony. For example, commentators speak in a basketball video, but we cannot visually find the speakers. In this paper, we tackle this issue by leveraging the cross-modal correspondence of audio and visual signals. We generate reliable event labels individually for each modality by swapping audio and visual tracks with other unrelated videos. If the original visual/audio data contain event clues, the event prediction from the newly assembled data would still be highly confident. In this way, we could protect our models from being misled by ambiguous event labels. In addition, we propose the cross-modal audio-visual contrastive learning to induce temporal difference on attention models within videos, i.e., urging the model to pick the current temporal segment from all context candidates. Experiments show we outperform state-of-the-art methods by a large margin.

关键词： Training Visualization computer vision Target tracking Annotations Predictive models pattern recognition

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