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Investigating the Impact of a Foundational Medical Image Model for CT Classification 19th

Investigating the Impact of a Foundational Medical Image Mod...

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19th International Symposium on Visual Computing

作者： Shah, Isha Mammadov, Tim Shehata, Mohamed Sami Rajapakshe, Rasika Univ British Columbia Okanagan Campus Kelowna BC Canada BC Canc Agcy Kelowna BC Canada

ISBN: (纸本)9783031773884;9783031773891

Cardiovascular disease (CVD) and breast cancer are the most prominent causes of mortality in the world [6]. Recent studies have shown that cancer survivors are more likely to develop and die from CVD than the general population. Screening co-morbidities like CVD is essential to lower the all-cause mortality rate for cancer patients. The CT scans obtained prior to breast cancer treatment offer an opportunity for simultaneous CVD risk estimation in at-risk patients. Deep learning is a powerful tool in image analysis, yet medical domain-specific foundational models are lacking. In this paper, we propose a comparative study on existing CT deep learning models namely DeepCAC [23], Tri2D-Net [5], SWIN-CT [4] and MedMAE [10] on a classification task for CVD mortality prediction for breast cancer patients on dataset consisting of similar to 5 million computed tomography images (CT slices). Although all models were trained on CT data, DeepCAC and Tri2D-Net are based on convolutional neural networks (CNN's), while SWIN-CT and MedMAE represent transformer architectures. Additionally, MedMAE was designed as a foundational medical model and was trained on a variety of medical image modalities using self-supervised learning. MedMAE achieves an accuracy of 93.21%, and the next best performing model, SWIN-CT had an accuracy of 81.76%. These results show that a foundational CT model can learn versatile representations from medical images which can be effectively transferred to downstream medical imaging tasks with higher accuracy, even when labelled data is scarce.

关键词： computer vision deep-learning early detection risk prediction cardiovascular disease foundational Model medical imaging

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FTC: A Novel Triplet Classification Model for Joint Entity and Relation Extraction 27th

FTC: A Novel Triplet Classification Model for Joint Entity ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Bin Zou, Chaofan Song, Wenwen School of Cyber Security and Computer Hebei University Baoding China Information Technology Center Hebei University Baoding China

ISBN: (纸本)9783031784941

Joint extraction of entities and relations from unstructured text is a crucial task in the construction of knowledge graph and natural language processing. Current methods simplify the implementation by breaking down joint extraction into modular stages. Despite their substantial performance, limitations such as cascading errors and redundancy in relational predictions persist. These issues arise from the oversight of the interdependence and inseparability of the three elements in a triple. In order to tackle these challenges, we introduce a novel Fine-Grained Triplet Classification Model(FTC) that relies on a score-based classifier and a tagging strategy for relation-specific upper triangular. The classifier evaluates if a token pair and relation form a factual triple, while the tagging strategy ensures straightforward and efficient decoding. In comprehensive experiments on four well-established datasets for relation triple extraction, FTC outperforms state-of-the-art baselines, consistently achieving performance improvements across diverse overlapping patterns and complex scenarios involving multiple triples. Our approach acknowledges the interconnected nature of entities and relations, mitigating cascading errors and reducing redundancy in predictions. Through this innovative perspective, the FTC demonstrates its effectiveness in enhancing the accuracy and efficiency of joint entity and relation extraction from unstructured text. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Redundancy

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EgoCoord: Self-calibrated Egocentric 3D Body Pose Estimation Using Pixel-Wise Coordinate Encoding 17th

EgoCoord: Self-calibrated Egocentric 3D Body Pose Estimation...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Lee, Jong-Bae Lee, Hyoung Lee, Beom-Ryeol Lee, Byung-Gook Son, Wook-Ho Electronics and Telecommunications Research Institute Daejeon Korea Republic of Department of Computer Engineering Dongseo University Busan Korea Republic of

ISBN: (纸本)9789819609000

The primary challenges for egocentric 3D human pose estimation techniques are the perspective and radial distortions introduced by fisheye lenses. Previous methods utilized camera calibration for undistortion or utilized neural networks to regress 3D human poses from distorted 2D poses. In this paper, we propose a novel approach that integrates a pixel-wise coordinate encoding technique for recognizing image distortion and utilizes the Vision Transformer (ViT) to extract distortion and pose tokens from the input image. The extracted tokens are used in a 3D volumetric heatmap-based egocentric pose estimator, which predicts the 3D human pose using pose tokens and performs pose correction using distortion tokens. The approach integrates CoordConv’s positional encoding strategies, neural network-based camera calibration methods, and the volumetric heatmap-based 3D human pose estimation method. We evaluate the proposed model’s performance using our new evaluation dataset and compare it with state-of-the-art models. Additionally, we perform an ablation study to demonstrate the individual effects of each module in the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pixels

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Improving Online Handwriting Recognition with Transfer Learning Using Out-of-Domain and Different-Dimensional Sources 27th

Improving Online Handwriting Recognition with Transfer Lear...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lee, Jiseok Akiba, Masaki Iwana, Brian Kenji Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (纸本)9783031781186

Online handwriting recognition is a widely used technique in our daily lives. Furthermore, deep learning has become one of the most popular and influential methods for online handwriting recognition. However, artificial neural networks typically require massive datasets. Transfer learning is a standard method to overcome the problem of lack of data. Usually, transfer learning works by initiating a network with trained weights and fine-tuning with a smaller dataset. Still, obtaining large amounts of online handwriting can be difficult for pre-training networks. Therefore, we propose pre-training with data sources with dimensions different from handwriting. Namely, we propose using univariate or multivariate data as a source dataset for two-dimensional target data by embedding out-of-domain time series of different dimensions into two-dimensional space. We evaluated the proposed method with four handwritten character datasets: a numerical digit dataset, an uppercase alphabet dataset, a lowercase alphabet dataset, and a Chinese character dataset. Through the evaluation, we demonstrate that transfer learning from datasets with a different dimensionality as online handwriting is possible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Tight Security Proof for SPHINCS⁺, Formally Verified 30th

A Tight Security Proof for SPHINCS<SUP>+</SUP>, Formally Ver...

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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Barbosa, Manuel Dupressoir, Francois Hulsing, Andreas Meijers, Matthias Strub, Pierre-Yves Univ Porto FCUP Porto Portugal INESC TEC Porto Portugal Max Planck Inst Secur & Privacy Bochum Germany Univ Bristol Bristol Avon England Eindhoven Univ Technol Eindhoven Netherlands SandboxAQ Palo Alto CA USA PQShield Paris France

ISBN: (纸本)9789819608935;9789819608942

SPHINCS+ is a post-quantum signature scheme that, at the time of writing, is being standardized as SLH-DSA. It is the most conservative option for post-quantum signatures, but the original tight proofs of security were flawed- as reported by Kudinov, Kiktenko and Fedorov in 2020. In this work, we formally prove a tight security bound for SPHINCS+ using the EasyCrypt proof assistant, establishing greater confidence in the general security of the scheme and that of the parameter sets considered for standardization. To this end, we reconstruct the tight security proof presented by Hulsing and Kudinov (in 2022) in a modular way. A small but important part of this effort involves a complex argument relating four different games at once, of a form not yet formalized in EasyCrypt (to the best of our knowledge). We describe our approach to overcoming this major challenge, and develop a general formal verification technique aimed at this type of reasoning. Enhancing the set of reusable EasyCrypt artifacts previously produced in the formal verification of stateful hash-based cryptographic constructions, we (1) improve and extend the existing libraries for hash functions and (2) develop new libraries for fundamental concepts related to hash-based cryptographic constructions, including Merkle trees. These enhancements, along with the formal verification technique we develop, further ease future formal verification endeavors in EasyCrypt, especially those concerning hash-based cryptographic constructions.

关键词： SPHINCS+ Post-Quantum Cryptography EasyCrypt Formal Verification Machine-Checked Proofs computer-Aided Cryptography

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Deterministic Synthesis of Defect Images Using Null Optimization 27th

Deterministic Synthesis of Defect Images Using Null Optimiz...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jo, Hyunwook Sahadewa, Marcellino Gazali, William Park, In Kyu Department of Electrical and Computer Engineering Inha University Incheon22212 Korea Republic of

ISBN: (纸本)9783031781711

In the manufacturing industry, defect classification is crucial but is hampered by challenges from imbalanced data, which often leads to model overfitting when data are scarce. One common solution is to use synthetic data from generative models;however, these models typically produce results that are structurally inconsistent. Addressing these concerns, this paper introduces a novel null embedding optimization technique that generates latent representations closely resembling the original images, significantly enhancing the fidelity of the generated images. This method ensures that synthetic images are not only visually similar to the original images but also subjected to a more extensive and diverse augmentation process, increasing the variability within the dataset. Consequently, this approach effectively doubles the usable dataset size and notably increases the accuracy of classification models by up to 11%. For those interested, the dataset and source code are accessible at https://***/ugiugi0823/DISN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Defect classification Diffusion Image synthesis

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Advanced Post-processing for Object Detection Dataset Generation 19th

Advanced Post-processing for Object Detection Dataset Genera...

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19th International Symposium on Visual Computing

作者： Poellabauer, Thomas Berkei, Sarah Knauthe, Volker Kuijper, Arjan Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt GRIS Darmstadt Germany

ISBN: (纸本)9783031773914;9783031773921

Fast acquisition and generation of training data is an important problem for the training of Deep Neural Networks (DNNs). Previous work using luminance keying for efficient training data acquisition achieves good results with very low effort, due to the high light absorption capabilities of the black background which enables keying through luminance instead of chroma. However, it does not reach real-world recording levels of performance. This paper significantly improves the achievable performance of luminance keying as evaluated for the use case of object detection. We introduce a novel post-processing pipeline incorporating a denoising diffusion probabilistic model (DDPM) to capitalize on lumakey recordings. First, we employ low rank adaptation (LoRA) to teach the recorded objects to a diffusion model. Second, we use Depth Anything to estimate the depth of the luma-key data. Third, utilizing ControlNet with depth estimates and Canny image filtering for guidance, we generate photo-realistic training images, using a wide range of relevant prompts, which increases model robustness in diverse environments. Applying the high quality masks of luminance keying, we get perfect ground truth for the object detection training. Extensive testing on the YCB-V object set demonstrates that our approach performs favorably compared to traditional techniques that require 3D meshes and material data, such as physically-based rendering or in-distribution dataset splits. Our proposed pipeline improves luminance keying to provide an efficient methodology for creating high-quality training datasets, facilitating the swift development and training of state-of-the-art DNNs for object detection, and is applicable to similar tasks, such as classification, and segmentation.

关键词： computer Vision Machine Learning Object Detection Synthetic Data

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J-Net: A Low-Resolution Lightweight Neural Network for Semantic Segmentation in the Medical Field for Embedded Deployment 19th

J-Net: A Low-Resolution Lightweight Neural Network for Seman...

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19th International Symposium on Visual Computing

作者： Ostrowski, Erik Shafique, Muhammad Tech Univ Wien TU Wien Inst Comp Engn Vienna Austria New York Univ Abu Dhabi Div Engn eBrain Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031773914;9783031773921

When deploying neural networks in real-life situations, the size and computational effort are often the limiting factors. This is especially true in environments where big, expensive hardware is not affordable, like in embedded medical devices, where budgets are often tight. State-of-the-art proposed multiple different lightweight solutions for such use cases, mostly by changing the base model architecture, not taking the input and output resolution into consideration. In this paper, we propose the J-Net architecture that takes advantage of the fact that in hardware-limited environments, we often refrain from using the highest available input resolutions to guarantee a higher throughput. Although using lower-resolution input leads to a significant reduction in computing and memory requirements, it may also incur reduced prediction quality. Our J-Net architecture addresses this problem by exploiting the fact that we can still utilize high-resolution ground-truths in training. The proposed model inputs lower-resolution images and high-resolution ground truths, which can improve the prediction quality by 5.5% while adding less than 200 parameters to the model. We conduct an extensive analysis to illustrate that J-Net enhances existing state-of-the-art frameworks for lightweight semantic segmentation of cancer in MRI images. We also tested the deployment speed of state-of-the-art lightweight networks and J-Net on Nvidia's Jetson Nano to emulate deployment in resource-constrained embedded scenarios. The framework is open-source and accessible online at https://***/ErikOstrowski/J-Net.

关键词： Semantic Segmentation Lightweight Embedded Deployment CAD computer Vision

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Assisted Data Annotation for Business Process Information Extraction from Textual Documents 30th

Assisted Data Annotation for Business Process Information E...

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30th International Conference on Cooperative Information Systems, CoopIS 2024

作者： Neuberger, Julian van der Aa, Han Ackermann, Lars Buschek, Daniel Herrmann, Jannic Jablonski, Stefan University of Bayreuth Bayreuth Germany University of Vienna Vienna Austria

ISBN: (纸本)9783031813740

Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can utilize business process management and its benefits. Yet, research towards this is severely restrained by an apparent lack of large and high-quality datasets. This lack of data can be attributed to, among other things, an absence of proper tool assistance for business process information extraction dataset creation, resulting in high workloads and inferior data quality. We explore two assistance features to support dataset creation, a recommendation system for identifying process information in the text and visualization of the current state of already identified process information as a graphical business process model. A controlled user study with 31 participants shows that assisting dataset creators with recommendations lowers all aspects of workload, up to -51.0%, and significantly improves annotation quality, up to +38.9% in F1 score. We make all data and code available to encourage further research on additional novel assistance strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Business process management Human computer interaction Natural language processing Process information extraction

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PPDAC: A Plug-and-Play Data Augmentation Component for Few-Shot Extractive Question Answering 23rd

PPDAC: A Plug-and-Play Data Augmentation Component for Few-...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Huang, Qi Fu, Han Luo, Wenbin Wang, Mingwen Luo, Kaiwei School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China School of Digital Industry Jiangxi Normal University Jiangxi Shangrao334000 China

ISBN: (纸本)9789819783663

Extractive Question Answering (EQA) in the few-shot learning scenario is one of the most challenging tasks of Machine Reading Comprehension (MRC). Some previous works employ external knowledge for data augmentation to improve the performance of few-shot extractive question answering. However, there are not always available external knowledge or language- and domain-specific NLP tools to deal with external knowledge such as part-of-speech taggers, syntactic parsers, and named-entity recognizers. In this paper, we present a novel Plug-and-Play Data Augmentation Component (PPDAC) for the few-shot extractive question answering, which includes a paraphrase generator and a paraphrase selector. Specifically, we generate multiple paraphrases of the question in the (question, passage, answer) triples using the paraphrase generator and then obtain highly similar statements via paraphrase selector to form more training data for fine-tuning. Extensive experiments on multiple EQA datasets show that our proposed plug-and-play data augmentation component significantly improves question-answering performance, and consistently outperforms state-of-the-art approaches in few-shot settings by a large margin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Question answering

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