pwc-archive/papers-with-abstracts huggingface dataset:
Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images
Authors: Memoona Aziz, Umair Rehman, Muhammad Umair Danish, Katarina Grolinger
Date: 2024-05-15 00:00:00
Tasks: Image Generation, MS-SSIM, SSIM

This paper introduces the Global-Local Image Perceptual Score (GLIPS), an image metric designed to assess the photorealistic image quality of AI-generated images with a high degree of alignment to human visual perception. Traditional metrics such as FID and KID scores do not align closely with human evaluations. The proposed metric incorporates advanced transformer-based attention mechanisms to assess local similarity and Maximum Mean Discrepancy (MMD) to evaluate global distributional similarity. To evaluate the performance of GLIPS, we conducted a human study on photorealistic image quality. Comprehensive tests across various generative models demonstrate that GLIPS consistently outperforms existing metrics like FID, SSIM, and MS-SSIM in terms of correlation with human scores. Additionally, we introduce the Interpolative Binning Scale (IBS), a refined scaling method that enhances the interpretability of metric scores by aligning them more closely with human evaluative standards. The proposed metric and scaling approach not only provides more reliable assessments of AI-generated images but also suggest pathways for future enhancements in image generation technologies.
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Authors: Markus R. Bujotzek, Ünal Akünal, Stefan Denner, Peter Neher, Maximilian Zenk, Eric Frodl, Astha Jaiswal, Moon Kim, Nicolai R. Krekiehn, Manuel Nickel, Richard Ruppel, Marcus Both, Felix Döllinger, Marcel Opitz, Thorsten Persigehl, Jens Kleesiek, Tobias Penzkofer, Klaus Maier-Hein, Rickmer Braren, Andreas Bucher
Date: 2024-05-15 00:00:00
Tasks: Federated Learning

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles, leaving behind a significant knowledge gap. Minding efforts to implement real-world FL, there is a notable lack of comprehensive assessment comparing FL to less complex alternatives. Materials & Methods: We extensively reviewed FL literature, categorizing insights along with our findings according to their nature and phase while establishing a FL initiative, summarized to a comprehensive guide. We developed our own FL infrastructure within the German Radiological Cooperative Network (RACOON) and demonstrated its functionality by training FL models on lung pathology segmentation tasks across six university hospitals. We extensively evaluated FL against less complex alternatives in three distinct evaluation scenarios. Results: The proposed guide outlines essential steps, identified hurdles, and proposed solutions for establishing successful FL initiatives conducting real-world experiments. Our experimental results show that FL outperforms less complex alternatives in all evaluation scenarios, justifying the effort required to translate FL into real-world applications. Discussion & Conclusion: Our proposed guide aims to aid future FL researchers in circumventing pitfalls and accelerating translation of FL into radiological applications. Our results underscore the value of efforts needed to translate FL into real-world applications by demonstrating advantageous performance over alternatives, and emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings.
Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations
Authors: Taha Emre, Arunava Chakravarty, Dmitrii Lachinov, Antoine Rivail, Ursula Schmidt-Erfurth, Hrvoje Bogunović
Date: 2024-05-15 00:00:00
Tasks: Contrastive Learning

Contrastive pretraining provides robust representations by ensuring their invariance to different image transformations while simultaneously preventing representational collapse. Equivariant contrastive learning, on the other hand, provides representations sensitive to specific image transformations while remaining invariant to others. By introducing equivariance to time-induced transformations, such as disease-related anatomical changes in longitudinal imaging, the model can effectively capture such changes in the representation space. In this work, we propose a Time-equivariant Contrastive Learning (TC) method. First, an encoder embeds two unlabeled scans from different time points of the same patient into the representation space. Next, a temporal equivariance module is trained to predict the representation of a later visit based on the representation from one of the previous visits and the corresponding time interval with a novel regularization loss term while preserving the invariance property to irrelevant image transformations. On a large longitudinal dataset, our model clearly outperforms existing equivariant contrastive methods in predicting progression from intermediate age-related macular degeneration (AMD) to advanced wet-AMD within a specified time-window.
Identity Overlap Between Face Recognition Train/Test Data: Causing Optimistic Bias in Accuracy Measurement
Authors: Haiyu Wu, Sicong Tian, Jacob Gutierrez, Aman Bhatta, Kağan Öztürk, Kevin W. Bowyer
Date: 2024-05-15 00:00:00
Tasks: Face Recognition

A fundamental tenet of pattern recognition is that overlap between training and testing sets causes an optimistic accuracy estimate. Deep CNNs for face recognition are trained for N-way classification of the identities in the training set. Accuracy is commonly estimated as average 10-fold classification accuracy on image pairs from test sets such as LFW, CALFW, CPLFW, CFP-FP and AgeDB-30. Because train and test sets have been independently assembled, images and identities in any given test set may also be present in any given training set. In particular, our experiments reveal a surprising degree of identity and image overlap between the LFW family of test sets and the MS1MV2 training set. Our experiments also reveal identity label noise in MS1MV2. We compare accuracy achieved with same-size MS1MV2 subsets that are identity-disjoint and not identity-disjoint with LFW, to reveal the size of the optimistic bias. Using more challenging test sets from the LFW family, we find that the size of the optimistic bias is larger for more challenging test sets. Our results highlight the lack of and the need for identity-disjoint train and test methodology in face recognition research.
SARATR-X: Toward Building A Foundation Model for SAR Target Recognition
Authors: Weijie Li, Wei Yang, Yuenan Hou, Li Liu, Yongxiang Liu, Xiang Li
Date: 2024-05-15 00:00:00
Tasks: 2D Object Detection, Earth Observation, Self-Supervised Learning

Despite the remarkable progress in synthetic aperture radar automatic target recognition (SAR ATR), recent efforts have concentrated on detecting and classifying a specific category, e.g., vehicles, ships, airplanes, or buildings. One of the fundamental limitations of the top-performing SAR ATR methods is that the learning paradigm is supervised, task-specific, limited-category, closed-world learning, which depends on massive amounts of accurately annotated samples that are expensively labeled by expert SAR analysts and have limited generalization capability and scalability. In this work, we make the first attempt towards building a foundation model for SAR ATR, termed SARATR-X. SARATR-X learns generalizable representations via self-supervised learning (SSL) and provides a cornerstone for label-efficient model adaptation to generic SAR target detection and classification tasks. Specifically, SARATR-X is trained on 0.18 M unlabelled SAR target samples, which are curated by combining contemporary benchmarks and constitute the largest publicly available dataset till now. Considering the characteristics of SAR images, a backbone tailored for SAR ATR is carefully designed, and a two-step SSL method endowed with multi-scale gradient features was applied to ensure the feature diversity and model scalability of SARATR-X. The capabilities of SARATR-X are evaluated on classification under few-shot and robustness settings and detection across various categories and scenes, and impressive performance is achieved, often competitive with or even superior to prior fully supervised, semi-supervised, or self-supervised algorithms. Our SARATR-X and the curated dataset are released at https://github.com/waterdisappear/SARATR-X to foster research into foundation models for SAR image interpretation.
Large coordinate kernel attention network for lightweight image super-resolution
Authors: Fangwei Hao, Jiesheng Wu, Haotian Lu, Ji Du, Jing Xu, Xiaoxuan Xu
Date: 2024-05-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

The multi-scale receptive field and large kernel attention (LKA) module have been shown to significantly improve performance in the lightweight image super-resolution task. However, existing lightweight super-resolution (SR) methods seldom pay attention to designing efficient building block with multi-scale receptive field for local modeling, and their LKA modules face a quadratic increase in computational and memory footprints as the convolutional kernel size increases. To address the first issue, we propose the multi-scale blueprint separable convolutions (MBSConv) as highly efficient building block with multi-scale receptive field, it can focus on the learning for the multi-scale information which is a vital component of discriminative representation. As for the second issue, we revisit the key properties of LKA in which we find that the adjacent direct interaction of local information and long-distance dependencies is crucial to provide remarkable performance. Thus, taking this into account and in order to mitigate the complexity of LKA, we propose a large coordinate kernel attention (LCKA) module which decomposes the 2D convolutional kernels of the depth-wise convolutional layers in LKA into horizontal and vertical 1-D kernels. LCKA enables the adjacent direct interaction of local information and long-distance dependencies not only in the horizontal direction but also in the vertical. Besides, LCKA allows for the direct use of extremely large kernels in the depth-wise convolutional layers to capture more contextual information, which helps to significantly improve the reconstruction performance, and it incurs lower computational complexity and memory footprints. Integrating MBSConv and LCKA, we propose a large coordinate kernel attention network (LCAN).
Progressive Depth Decoupling and Modulating for Flexible Depth Completion
Authors: Zhiwen Yang, Jiehua Zhang, Liang Li, Chenggang Yan, Yaoqi Sun, Haibing Yin
Date: 2024-05-15 00:00:00
Tasks: Depth Completion

Image-guided depth completion aims at generating a dense depth map from sparse LiDAR data and RGB image. Recent methods have shown promising performance by reformulating it as a classification problem with two sub-tasks: depth discretization and probability prediction. They divide the depth range into several discrete depth values as depth categories, serving as priors for scene depth distributions. However, previous depth discretization methods are easy to be impacted by depth distribution variations across different scenes, resulting in suboptimal scene depth distribution priors. To address the above problem, we propose a progressive depth decoupling and modulating network, which incrementally decouples the depth range into bins and adaptively generates multi-scale dense depth maps in multiple stages. Specifically, we first design a Bins Initializing Module (BIM) to construct the seed bins by exploring the depth distribution information within a sparse depth map, adapting variations of depth distribution. Then, we devise an incremental depth decoupling branch to progressively refine the depth distribution information from global to local. Meanwhile, an adaptive depth modulating branch is developed to progressively improve the probability representation from coarse-grained to fine-grained. And the bi-directional information interactions are proposed to strengthen the information interaction between those two branches (sub-tasks) for promoting information complementation in each branch. Further, we introduce a multi-scale supervision mechanism to learn the depth distribution information in latent features and enhance the adaptation capability across different scenes. Experimental results on public datasets demonstrate that our method outperforms the state-of-the-art methods. The code will be open-sourced at [this https URL](https://github.com/Cisse-away/PDDM).
Application of Gated Recurrent Units for CT Trajectory Optimization
Authors: Yuedong Yuan, Linda-Sophie Schneider, Andreas Maier
Date: 2024-05-15 00:00:00
Tasks: Computed Tomography (CT), SSIM

Recent advances in computed tomography (CT) imaging, especially with dual-robot systems, have introduced new challenges for scan trajectory optimization. This paper presents a novel approach using Gated Recurrent Units (GRUs) to optimize CT scan trajectories. Our approach exploits the flexibility of robotic CT systems to select projections that enhance image quality by improving resolution and contrast while reducing scan time. We focus on cone-beam CT and employ several projection-based metrics, including absorption, pixel intensities, contrast-to-noise ratio, and data completeness. The GRU network aims to minimize data redundancy and maximize completeness with a limited number of projections. We validate our method using simulated data of a test specimen, focusing on a specific voxel of interest. The results show that the GRU-optimized scan trajectories can outperform traditional circular CT trajectories in terms of image quality metrics. For the used specimen, SSIM improves from 0.38 to 0.49 and CNR increases from 6.97 to 9.08. This finding suggests that the application of GRU in CT scan trajectory optimization can lead to more efficient, cost-effective, and high-quality imaging solutions.
Deep Blur Multi-Model (DeepBlurMM) -- a strategy to mitigate the impact of image blur on deep learning model performance in histopathology image analysis
Authors: Yujie Xiang, Bojing Liu, Mattias Rantalainen
Date: 2024-05-15 00:00:00
Tasks: Binary Classification, whole slide images

AI-based analysis of histopathology whole slide images (WSIs) is central in computational pathology. However, image quality, including unsharp areas of WSIs, impacts model performance. We investigate the impact of blur and propose a multi-model approach to mitigate negative impact of unsharp image areas. In this study, we use a simulation approach, evaluating model performance under varying levels of added Gaussian blur to image tiles from >900 H&E-stained breast cancer WSIs. To reduce impact of blur, we propose a novel multi-model approach (DeepBlurMM) where multiple models trained on data with variable amounts of Gaussian blur are used to predict tiles based on their blur levels. Using histological grade as a principal example, we found that models trained with mildly blurred tiles improved performance over the base model when moderate-high blur was present. DeepBlurMM outperformed the base model in presence of moderate blur across all tiles (AUC:0.764 vs. 0.710), and in presence of a mix of low, moderate, and high blur across tiles (AUC:0.821 vs. 0.789). Unsharp image tiles in WSIs impact prediction performance. DeepBlurMM improved prediction performance under some conditions and has the potential to increase quality in both research and clinical applications.
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Authors: Nima Fathi, Amar Kumar, Brennan Nichyporuk, Mohammad Havaei, Tal Arbel
Date: 2024-05-15 00:00:00
Tasks: counterfactual, Image Generation

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.
MVBIND: Self-Supervised Music Recommendation For Videos Via Embedding Space Binding
Authors: Jiajie Teng, Huiyu Duan, Yucheng Zhu, Sijing Wu, Guangtao Zhai
Date: 2024-05-15 00:00:00
Tasks: Cross-Modal Retrieval, Music Recommendation, Retrieval

Recent years have witnessed the rapid development of short videos, which usually contain both visual and audio modalities. Background music is important to the short videos, which can significantly influence the emotions of the viewers. However, at present, the background music of short videos is generally chosen by the video producer, and there is a lack of automatic music recommendation methods for short videos. This paper introduces MVBind, an innovative Music-Video embedding space Binding model for cross-modal retrieval. MVBind operates as a self-supervised approach, acquiring inherent knowledge of intermodal relationships directly from data, without the need of manual annotations. Additionally, to compensate the lack of a corresponding musical-visual pair dataset for short videos, we construct a dataset, SVM-10K(Short Video with Music-10K), which mainly consists of meticulously selected short videos. On this dataset, MVBind manifests significantly improved performance compared to other baseline methods. The constructed dataset and code will be released to facilitate future research.
Graph Neural Network based Handwritten Trajectories Recognition
Authors: Anuj Sharma, Sukhdeep Singh, S Ratna
Date: 2024-05-15 00:00:00
Tasks: Graph Neural Network, Handwriting Recognition

The graph neural networks has been proved to be an efficient machine learning technique in real life applications. The handwritten recognition is one of the useful area in real life use where both offline and online handwriting recognition are required. The chain code as feature extraction technique has shown significant results in literature and we have been able to use chain codes with graph neural networks. To the best of our knowledge, this work presents first time a novel combination of handwritten trajectories features as chain codes and graph neural networks together. The handwritten trajectories for offline handwritten text has been evaluated using recovery of drawing order, whereas online handwritten trajectories are directly used with chain codes. Our results prove that present combination surpass previous results and minimize error rate in few epochs only.
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what
Authors: Henri Bouma, Bart Joosten, Maarten C Kruithof, Maaike H T de Boer, Alexandru Ginsca, Benjamin Labbe, Quoc T Vuong
Date: 2024-05-15 00:00:00
Tasks: Active Learning, geo-localization, Novel Concepts

Due to the increasing need for effective security measures and the integration of cameras in commercial products, a hugeamount of visual data is created today. Law enforcement agencies (LEAs) are inspecting images and videos to findradicalization, propaganda for terrorist organizations and illegal products on darknet markets. This is time consuming.Instead of an undirected search, LEAs would like to adapt to new crimes and threats, and focus only on data from specificlocations, persons or objects, which requires flexible interpretation of image content. Visual concept detection with deepconvolutional neural networks (CNNs) is a crucial component to understand the image content. This paper has fivecontributions. The first contribution allows image-based geo-localization to estimate the origin of an image. CNNs andgeotagged images are used to create a model that determines the location of an image by its pixel values. The secondcontribution enables analysis of fine-grained concepts to distinguish sub-categories in a generic concept. The proposedmethod encompasses data acquisition and cleaning and concept hierarchies. The third contribution is the recognition ofperson attributes (e.g., glasses or moustache) to enable query by textual description for a person. The person-attributeproblem is treated as a specific sub-task of concept classification. The fourth contribution is an intuitive image annotationtool based on active learning. Active learning allows users to define novel concepts flexibly and train CNNs with minimalannotation effort. The fifth contribution increases the flexibility for LEAs in the query definition by using query expansion.Query expansion maps user queries to known and detectable concepts. Therefore, no prior knowledge of the detectableconcepts is required for the users. The methods are validated on data with varying locations (popular and non-touristiclocations), varying person attributes (CelebA dataset), and varying number of annotations.
Scalable Image Coding for Humans and Machines Using Feature Fusion Network
Authors: Takahiro Shindo, Taiju Watanabe, Yui Tatsumi, Hiroshi Watanabe
Date: 2024-05-15 00:00:00
Tasks: Image Compression

As image recognition models become more prevalent, scalable coding methods for machines and humans gain more importance. Applications of image recognition models include traffic monitoring and farm management. In these use cases, the scalable coding method proves effective because the tasks require occasional image checking by humans. Existing image compression methods for humans and machines meet these requirements to some extent. However, these compression methods are effective solely for specific image recognition models. We propose a learning-based scalable image coding method for humans and machines that is compatible with numerous image recognition models. We combine an image compression model for machines with a compression model, providing additional information to facilitate image decoding for humans. The features in these compression models are fused using a feature fusion network to achieve efficient image compression. Our method's additional information compression model is adjusted to reduce the number of parameters by enabling combinations of features of different sizes in the feature fusion network. Our approach confirms that the feature fusion network efficiently combines image compression models while reducing the number of parameters. Furthermore, we demonstrate the effectiveness of the proposed scalable coding method by evaluating the image compression performance in terms of decoded image quality and bitrate.