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IMIL: Interactive Medical Image Learning Framework

IMIL: Interactive Medical Image Learning Framework

引用

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

作者： Adrit Rao Andrea Fisher Ken Chang John Christopher Panagides Katherine McNamara Joon-Young Lee Oliver Aalami Stanford University Palo Alto High School Adobe Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data augmentations are widely used in training medical image deep learning models to increase the diversity and size of sparse datasets. However, commonly used augmentation techniques can result in loss of clinically relevant information from medical images, leading to incorrect predictions at inference time. We propose the Interactive Medical Image Learning (IMIL) framework, a novel approach for improving the training of medical image analysis algorithms that enables clinician-guided intermediate training data augmentations on misprediction outliers, focusing the algorithm on relevant visual information. To prevent the model from using irrelevant features during training, IMIL will ’blackout’ clinician-designated irrelevant regions and replace the original images with the augmented samples. This ensures that for originally mispredicted samples, the algorithm subsequently attends only to relevant regions and correctly correlates them with the respective diagnosis. We validate the efficacy of IMIL using radiology residents and compare its performance to state-of-the-art data augmentations. A 4.2% improvement in accuracy over ResNet-50 was observed when using IMIL on only 4% of the training set. Our study demonstrates the utility of clinician-guided interactive training to achieve meaningful data augmentations for medical image analysis algorithms.

关键词： Training computer vision Image analysis Accuracy Computational modeling Radiology Data augmentation

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PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model

PointPrompt: A Multi-modal Prompting Dataset for Segment Any...

引用

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

作者： Jorge Quesada Mohammad Alotaibi Mohit Prabhushankar Ghassan AlRegib OLIVES Lab Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The capabilities of foundation models, most recently the Segment Anything Model, have gathered a large degree of attention for providing a versatile framework for tackling a wide array of image segmentation tasks. However, the interplay between human prompting strategies and the segmentation performance of these models remains understudied, as does the role played by the domain knowledge that humans (by previous exposure) and models (by pretraining) bring to the prompting process. To bridge this gap, we present the PointPrompt dataset compiled across multiple image modalities as well as multiple prompting annotators per modality. We collected a total of 16 image datasets from the natural, underwater, medical and seismic domain in order to create a comprehensive resource to facilitate the study of prompting behavior and agreement across modalities. Overall, our prompting dataset contains 158880 inclusion points and 52594 exclusion points over a total of 6000 images. Our analysis highlights the following: (i) viability of prompts across heterogeneous data, (ii) that point prompts are a valuable resource in the effort for enhancing the robustness and generalizability of segmentation models across diverse domains, (iii) prompts facilitate an understanding of the dynamics between annotation strategies and neural network outcomes. Information on downloading the dataset, images, and prompting tool is provided on our project website https://***/pointprompt/.

关键词： Bridges Image segmentation computer vision Computational modeling conferences Neural networks Dynamic scheduling

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Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

Distribution-Aware Multi-Label FixMatch for Semi-Supervised ...

引用

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

作者： Sontje Ihler Felix Kuhnke Timo Kuhlgatz Thomas Seel Institute of Mechatronic Systems Leibniz Universität Hannover

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Semi-supervised learning (SSL) has achieved remarkable success for multiclass classification in recent years, yielding a promising solution for medical image classification where labeled data is scarce but unlabeled images are accessible. In the context of multi-label problems however, SSL is still under-explored. In this work we adapt Fix-Match to the multi-label scenario, specifically focusing on CheXpert, a multi-label chest X-ray classification dataset which is imbalanced and only partially labeled. Leveraging distribution alignment, our proposed method, ML-FixMatch+DA, achieves solid performance gains in SSL tasks (AUC: +2.6%) and in a missing label scenario (AUC: +1.9%). In contrast to previous work we achieve a performance gain on CheXpert using FixMatch. We show that in contrast to multiclass FixMatch, where distribution alignment is optional, it is essential for multi-label FixMatch to handle class imbalance and generate reliable (positive and negative) pseudo-labels. Our pseudo-label selection is based on a single threshold for all classes and handles imbalance with no prior knowledge on label distributions. Our adaptation keeps the simplicity of the original multi-class FixMatch with no added hyperparameters (even for imbalanced data) and demonstrates the feasibility of simple SSL for multi-label problems, filling a crucial gap in the literature.

关键词： Semisupervised learning Performance gain Solids Filling Robustness pattern recognition Mirrors

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Tackling Domain Shifts in Person Re-Identification: A Survey and Analysis

Tackling Domain Shifts in Person Re-Identification: A Survey...

引用

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

作者： Vuong D. Nguyen Samiha Mirza Abdollah Zakeri Ayush Gupta Khadija Khaldi Rahma Aloui Pranav Mantini Shishir K. Shah Fatima Merchant University of Houston Johns Hopkins University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The necessity for a Person ReID system for rapidly evolving urban surveillance applications is severely challenged by domain shifts—variations in data distribution that occur across different environments or times. In this paper, we provide the first empirical review of domain shift in person ReID, which includes three settings namely Unsupervised Domain Adaptation ReID, Domain Generalizable ReID, and Lifelong ReID. We observe that existing approaches only tackle domain shifts caused by cross-dataset setting, while ignoring intra-dataset attribute domain shifts caused by changes in clothing, shape, or gait, which is very common in ReID. Thus, we enhance research directions in this field by redefining domain shift in ReID as the combination of attribute domain shift with cross-dataset domain shift. With a focus on Lifelong Re-ID methods, we conduct an extensive comparison on a fair experimental setup and provide an in-depth analysis of these methods under both non-cloth-changing and cloth-changing Re-ID scenarios. Insights into the strengths and limitations of these methods based on their performance are studied. This paper outlines future research directions and paves the way for the development of more adaptive, resilient, and enduring cross-domain ReID systems. Code is available here.

关键词： Surveys computer vision Codes Shape Reviews Surveillance conferences

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3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

3D Human Pose Estimation with Occlusions: Introducing BlendM...

引用

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

作者： Filipa Lino Carlos Santiago Manuel Marques LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of 3D Human Pose Estimation (HPE), accurately estimating human pose, especially in scenarios with occlusions, is a significant challenge. This work identifies and addresses a gap in the current state of the art in 3D HPE concerning the scarcity of data and strategies for handling occlusions. We introduce our novel BlendMimic3D dataset, designed to mimic real-world situations where occlusions occur for seamless integration in 3D HPE algorithms. Additionally, we propose a 3D pose refinement block, employing a Graph Convolutional Network (GCN) to enhance pose representation through a graph model. This GCN block acts as a plug-and-play solution, adaptable to various 3D HPE frameworks without requiring retraining them. By training the GCN with occluded data from Blend-Mimic3D, we demonstrate significant improvements in resolving occluded poses, with comparable results for non-occluded ones. Project web page is available at https://***/BlendMimic3D/.

关键词： Training Solid modeling computer vision Three-dimensional displays Graph convolutional networks conferences Pose estimation

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Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images.

Dr-SAM: An End-to-End Framework for Vascular Segmentation, D...

引用

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

作者： Vazgen Zohranyan Vagner Navasardyan Hayk Navasardyan Jan Borggrefe Shant Navasardyan Yerevan State University (YSU) ServiceTitan Inc. Ruhr-Universität Bochum (RUB) Johannes Wesling University Hospital Department of Radiology Neuroradiology and Nuclear Medicine Synopsys Armenia CJSC Picsart AI Research (PAIR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in AI have significantly transformed medical imaging, particularly in angiography, by enhancing diagnostic precision and patient care. However existing works are limited in analyzing the aorta and iliac arteries, above all for vascular anomaly detection and characterization. To close this gap, we propose Dr-SAM, a comprehensive multi-stage framework for vessel segmentation, diameter estimation, and anomaly analysis aiming to examine the peripheral vessels through angiography images. For segmentation we introduce a customized positive/negative point selection mechanism applied on top of the Segment Anything Model (SAM), specifically for medical (Angiography) images. Then we propose a morphological approach to determine the vessel diameters followed by our histogram-driven anomaly detection approach. Moreover, we introduce a new benchmark dataset for the comprehensive analysis of peripheral vessel angiography images which we hope can boost the upcoming research in this direction leading to enhanced diagnostic precision and ultimately better health outcomes for individuals facing vascular issues.

关键词： Image segmentation Angiography conferences Estimation Benchmark testing pattern recognition Anomaly detection

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T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation

T2VBench: Benchmarking Temporal Dynamics for Text-to-Video G...

引用

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

作者： Pengliang Ji Chuyang Xiao Huilin Tai Mingxiao Huo Carnegie Mellon University ShanghaiTech University McGill University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-video (T2V) generative models produce exceptionally realistic videos, they lack a comprehensive evaluation across the temporal dimension, with a limited focus on basic dynamics including camera transitions, movement, and event sequences. In this work, we introduce T2VBench, a comprehensive T2V evaluation benchmark enriched with temporal dynamics lexicons derived from curated temporal words on Wikipedia. T2VBench is a hierarchical evaluation framework comprising over 1,600 temporally rich prompts and 5,000 generated videos with human ratings, spanning 16 critical temporal evaluation dimensions. We assess three leading text-to-video models, including ZeroScope and Pika, to gauge their proficiency in handling temporal dynamics. Our analysis highlights the strengths and limitations of these models across various temporal aspects. Furthermore, we provide insights into future directions for enhancing text-to-video evaluation metrics and offer a detailed analysis of these models’ performance across the temporal dimensions. Overall, T2VBench is the first-of-its-kind comprehensive benchmark fully focused on temporal dynamics for text-to-video evaluation. It aims to facilitate scientific benchmarking of both generative models and automated metrics on text-to-video generation.

关键词： Measurement Analytical models Computational modeling conferences Encyclopedias Benchmark testing pattern recognition

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DECNet: A Non-Contacting Dual-Modality Emotion Classification Network for Driver Health Monitoring

DECNet: A Non-Contacting Dual-Modality Emotion Classificatio...

引用

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

作者： Zhekang Dong Chenhao Hu Shiqi Zhou Liyan Zhu Junfan Wang Yi Chen Xudong Lv Xiaoyue Ji Hangzhou Dianzi University Zhejiang Provincial Key Laboratory of Equipment Electronics Zhejiang University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Negative emotions have been identified as significant factors influencing driver behavior, easily leading to extremely serious traffic accidents. Hence, there is a pressing need to develop an automatic emotion classification method for driver health monitoring and road safety improvement. Most of the existing methods predominantly focus on single modalities, resulting in suboptimal classification performance due to the underutilization of heterogeneous information. In this work, we propose a novel non-contacting dual-modality driver emotion classification network (DECNet) to address these limitations. DECNet consists of three key modules: 1) facial video modality processing module; 2) driving behavior modality processing module; 3) fusion decision module. Meanwhile, we introduce a combined multi-task learning strategy within DECNet to improve the efficacy in the driver emotion classification task. To evaluate the effectiveness of the proposed DECNet, we conducted experiments on the PPB-Emo dataset, the experimental results showcase the superiority in terms of accuracy (⩾ 6.12% Acc-7) and F1-score (⩾ 7.25% F1-7) compared to existing state-of-the art methods. The model and code will be available at https://***/fqfqngxhs/***

关键词： Smart cities Pressing Feature extraction Multitasking Road safety Safety pattern recognition

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LSDIR: A Large Scale Dataset for Image Restoration

LSDIR: A Large Scale Dataset for Image Restoration

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

作者： Yawei Li Kai Zhang Jingyun Liang Jiezhang Cao Ce Liu Rui Gong Yulun Zhang Hao Tang Yun Liu Denis Demandolx Rakesh Ranjan Radu Timofte Luc Van Gool Computer Vision Lab ETH Zürich Meta Reality Labs University of Würzburg KU Leuven

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks ‘only’ contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small training set limits the performance of image restoration networks. To solve this problem, we collect high-resolution (HR) images from Flickr for image restoration. To ensure the pixel-level quality of the collected dataset, annotators were invited to manually inspect each of the collected image and remove the low-quality ones. The final dataset contains 84,991 high-quality training images, 1,000 validation images, and 1,000 test images. In addition, we showed that the model capacity of large networks could be fully exploited by training on the large scale dataset with significantly increased patch size and prolonged training iterations. The experimental results on image super-resolution (SR), denoising, JPEG deblocking, deblurring, and demosaicking, and real-world SR show that image restoration networks benefit a lot from the large scale dataset.

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T2FNorm: Train-time Feature Normalization for OOD Detection in Image Classification

T2FNorm: Train-time Feature Normalization for OOD Detection ...

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

作者： Sudarshan Regmi Bibek Panthi Sakar Dotel Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal Tribhuvan University Nepal West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit this potential. Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance. In this work, we introduce T2FNorm, a novel approach to transforming features to hyperspherical space during training, while employing non-transformed space for OOD-scoring purposes. This method yields a surprising enhancement in OOD detection capabilities without compromising model accuracy in in-distribution(ID). Our investigation demonstrates that the proposed technique substantially diminishes the norm of the features of all samples, more so in the case of out-of-distribution samples, thereby addressing the prevalent concern of overconfidence in neural networks. The proposed method also significantly improves various post-hoc OOD detection methods.

关键词： Training Measurement Deep learning computer vision Accuracy conferences Neural networks

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