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检索条件"任意字段=22nd Brazilian Symposium on Computer Graphics and Image Processing"

共 267 条记录，以下是11-20 订阅

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Towards Multi-Label Open Set Diagnosis for Medical images

Towards Multi-Label Open Set Diagnosis for Medical Images

引用

IEEE International symposium on Biomedical Imaging

作者： Shijie Pang Yuze Zhao Rui-Wei Zhao Rui Feng Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label Data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep Learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection.

关键词： Training Uncertainty image recognition Face recognition Training data Multi label classification Robustness Medical diagnosis Medical diagnostic imaging Diseases

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Contrastive Pretraining for Computational Pathology with Visual-Language Models

Contrastive Pretraining for Computational Pathology with Vis...

引用

IEEE International symposium on Biomedical Imaging

作者： Qifeng Zhou Thao M. Dang Yuzhi Guo Hehuan Ma Wenliang Zhong Saiyang Na Jean Gao Junzhou Huang Department of Computer Science and Engineering The University of Texas at Arlington Arlington USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In computational pathology, effectively capturing visual-language embeddings from extensive pathology image-text pairs has become increasingly crucial for diverse downstream tasks. Although prior studies have fine-tuned models like CLIP using large pathology image-text datasets, these models encounter limitations due to their separate processing of text and images, restricting their ability to capture essential cross-modal relationships critical in pathology. Recent advancements in large language models (LLMs) have led to the development of vision-language models (VLMs) that demonstrate enhanced multimodal capabilities, including stronger language comprehension and reasoning skills compared to CLIP. However, while VLMs show potential for multimodal embedding, previous efforts have primarily focused on text-based tasks, leaving their application to multimodal pathology data largely unexplored. In this work, we introduce a VLM-based framework designed to integrate and align pathology visual-language embeddings within a single model. We validate our framework's effectiveness through cross-modal retrieval on pathology image-caption datasets and zero-shot patch classification across seven pathology image datasets, demonstrating its superiority over CLIP-based models and underscoring its potential for advancing pathology research.

关键词： Pathology Computational modeling Biological system modeling Large language models Zero shot learning Contrastive learning Cognition Cross modal retrieval Biomedical imaging

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Dineuro: Distilling Knowledge from 2D Natural images via Deformable Tubular Transferring Strategy for 3D Neuron Reconstruction

Dineuro: Distilling Knowledge from 2D Natural Images via Def...

引用

IEEE International symposium on Biomedical Imaging

作者： Yik San Cheng Runkai Zhao Heng Wang Hanchuan Peng Yui Lo Yuqian Chen Lauren J. O'Donnell Weidong Cai School of Computer Science The University of Sydney Sydney Australia SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University Nanjing China Harvard Medical School Boston MA USA Brigham and Women's Hospital Boston USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Reconstructing neuron morphology from 3D light microscope imaging data is critical to aid neuroscientists in analyzing brain networks and neuroanatomy. With the boost from deep learning techniques, a variety of learning-based segmentation models have been developed to enhance the signal-to-noise ratio of raw neuron images as a pre-processing step in the reconstruction workflow. However, most existing models directly encode the latent representative features of volumetric neuron data but neglect their intrinsic morphological knowledge. To address this limitation, we design a novel framework, namely DINeuro, that distills the prior knowledge from DINO which pre-trained on extensive 2D natural images to facilitate neuronal morphological learning of our 3D Vision Transformer. To bridge the knowledge gap between the 2D natural image and 3D microscopic morphologic domains, we propose a deformable tubular transferring strategy that adapts the pre-trained 2D natural knowledge to the inherent tubular characteristics of neuronal structure in the latent embedding space. The experimental results on the Janelia dataset of the BigNeuron project demonstrate that our method achieves a segmentation performance improvement of 4.53% in mean Dice and 3.56% in mean 95% Hausdorff distance.

关键词： image segmentation computer vision Three-dimensional displays Microscopy Neurons Morphology Transformers Feature extraction Brain modeling image reconstruction

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Generating Seamless Virtual Immunohistochemical Whole Slide images with Content and Color Consistency

Generating Seamless Virtual Immunohistochemical Whole Slide ...

引用

IEEE International symposium on Biomedical Imaging

作者： Sitong Liu Kechun Liu Samuel Margolis Wenjun Wu Stevan R. Knezevich David E. Elder Megan M. Eguchi Joann G. Elmore Linda G. Shapiro University of Washington University of California Los Angeles Pathology Associates Clovis CA USA University of Pennsylvania

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. However, obtaining IHC staining is not always readily available and often costly. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) produces useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture’ and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder the adoption of virtual WSI and accurate clinical assessment. To address this limitation, this paper proposes a novel consistent whole slide image (WSI) synthesis network, CC-WSI-Net, which generates seamless and realistic synthetic WSIs from hematoxylin and eosin (H&E)-stained images. Our method addresses the limitations of current virtual WSI generation methods by integrating a content and color consistency supervisor, ensuring consistency across tiles and accurate detection of clinically relevant features. CC-WSI-Net is validated via image quality analysis, detection assessments, and a survey conducted with pathologists. Notably, in 67% of cases, pathologists could not distinguish between traditionally stained slides and the synthetic slides from CC-WSI-Net.

关键词： image quality Surveys Accuracy image color analysis Feature extraction computer aided diagnosis Medical diagnostic imaging Immune system Diseases

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EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical image Analysis

EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neura...

引用

IEEE International symposium on Biomedical Imaging

作者： Yi Pan Hanqi Jiang Junhao Chen Yiwei Li Huaqin Zhao Yifan Zhou Peng Shu Zihao Wu Zhengliang Liu Dajiang Zhu Xiang Li Yohannes Abate Tianming Liu School of Computing University of Georgia Athens GA USA Department of Computer Science and Engineering University of Texas at Arlington TX USA Department of Radiology Massachusetts General Hospital and Harvard Medical School MA USA Department of Physics and Astronomy University of Georgia Athens GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial neural networks (ANNs), predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. SNNs are more energy-efficient and excel at processing spatio-temporal data than ANNs. Significant advancements have been made in SNN-based convolutional neural networks (CNNs) and Transformer architectures. However, neuromorphic computing for the medical imaging domain remains underexplored. In this study, we introduce EG-SpikeFormer, an SNN architecture tailored for clinical tasks that incorporates eye-gaze data to guide the model's attention to the diagnostically relevant regions in medical images. Our developed approach effectively addresses shortcut learning issues commonly observed in conventional models, especially in scenarios with limited clinical data and high demands for model reliability, generalizability, and transparency. Our EG-SpikeFormer not only demonstrates superior energy efficiency and performance in medical image prediction tasks but also enhances clinical relevance through multi-modal information alignment. By incorporating eye-gaze data, the model improves interpretability and generalization, opening new directions for applying neuromorphic computing in medical image analysis.

关键词： image analysis Neuromorphic engineering Computational modeling Spiking neural networks Medical services Transformers Energy efficiency Data models Convolutional neural networks Medical diagnostic imaging

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RGBN image editing

RGBN image editing

引用

22nd brazilian symposium on computer graphics and image processing

作者： Pereira, Thiago Velho, Luiz IMPA Visgraf Lab Rio De Janeiro Brazil

ISBN: (纸本)9781424449781

We propose a method to edit RGBNs (images with a color and a normal channel). High resolution RGBNs are easy to obtain using photometric stereo. Free editing will result in normals which do not correspond to any realizable surface. Our normal operators guarantee the integrability of the results. Our method can filter normals with any linear kernel allowing high-pass and edge-enhancement filters. We have designed a normal linear combination method for adding details with frequency control. New geometric features can be created with a custom brush that warps deformations along its path. A nonlinear operator is also proposed. Its integrability is controlled with a two-band separation of frequencies: smooth shape and details.

关键词： RGBN normal map gradient reconstruction normal processing filtering deformation

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Fractal-JSEG: JSEG Using an Homogeneity Measurement Based on Local Fractal Descriptor

Fractal-JSEG: JSEG Using an Homogeneity Measurement Based on...

引用

22nd brazilian symposium on computer graphics and image processing

作者： Komati, Karin S. Salles, Evandro O. T. Sarcinelli Filho, Mario Univ Fed Espirito Santo Grad Program Elect Engn Vitoria Brazil

ISBN: (纸本)9781424449781

This paper proposes an improved version for the JSEG color image segmentation algorithm, combining the classical JSEG algorithm and a local fractal operator that measures the fractal dimension of each pixel, thus improving the boundary detection in the J-map. Experiments with natural color images of the Berkeley Segmentation Dataset and Benchmark are presented, which show improved results in comparison with the classical JSEG algorithm.

关键词： color image segmentation J value segmentation (JSEG) edge detection fractal dimension local fractal operator differential box-counting (DBC)

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Mammography images Restoration by Quantum Noise Reduction and Inverse MTF Filtering

Mammography Images Restoration by Quantum Noise Reduction an...

引用

22nd brazilian symposium on computer graphics and image processing

作者： dos Santos Romualdo, Larissa Cristina da Costa Vieira, Marcelo Andrade Schiabel, Homero Univ Sao Paulo Dept Engn Eletr Sao Carlos SP Brazil

ISBN: (纸本)9781424449781

This work proposes a new restoration method to improve mammographic images by using Anscombe Transform and Wiener Filter to quantum noise reduction. Besides, it is performed an image enhancement by using a restoration inverse filter, calculated based on the image system modulation transfer function (MTF). This pre-processing technique were used for a set of mammographic phantom images in order measure the number of microcalcifications correctly detected by a computer-aided detection (CAD) algorithm. Results showed that the proposed method improved breast images quality by overcome the acquisition process constrains and reducing noise. The performance of the breast microcalcification CAD was improved when using the restored images set in comparison to the original one.

关键词： image restoration mammography Anscombe transform quantum noise

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A Regularized Nonlinear Diffusion Approach for Texture image Denoising

A Regularized Nonlinear Diffusion Approach for Texture Image...

引用

22nd brazilian symposium on computer graphics and image processing

作者： Casaca, Wallace Correa de O. Boaventura, Maurilio Sao Paulo State Univ Inst Biosci Humanities & Exact Sci Dept Comp Sci & Stat Sao Jose Do Rio Preto SP Brazil

ISBN: (纸本)9781424449781

In this paper a new partial differential equation based method is presented with a view to denoising images having textures. The proposed model combines a nonlinear anisotropic diffusion filter with recent harmonic analysis techniques. A wave atom shrinkage allied to detection by gradient technique is used to guide the diffusion process so as to smooth and maintain essential image characteristics. Two forcing terms are used to maintain and improve edges, boundaries and oscillatory features of an image having irregular details and texture. Experimental results show the performance of our model for texture preserving denoising when compared to recent methods in literature.

关键词： image denoising

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Optimized Pattern-Based Adaptive Mesh Refinement Using GPU

Optimized Pattern-Based Adaptive Mesh Refinement Using GPU

引用

22nd brazilian symposium on computer graphics and image processing

作者： Lenz, Ricardo Cavalcante-Neto, Joaquim Bento Vidal, Creto Augusto Univ Fed Ceara Fortaleza Ceara Brazil

ISBN: (纸本)9781424449781

The high performance of GPUs and the increasing use of their programming mechanisms have fostered the development of graphics applications that better exploit the raw power of these devices to achieve higher levels of realism. Silhouette refinement, as one of the techniques that help to improve realism, has profited from GPUs' advances in recent years. In this paper, we present a method for triangular mesh refinement which alleviates the problem of rugged silhouettes. We demonstrate that, through a clever indexing scheme, our method is able to use adaptive patterns in an optimized way, taking full advantage of the GPU's parallelism. The ideas we used were adapted from distinct previous works, but our method presents astounding performance gains. Also, our method works very well with existing meshes, therefore, it can improve the visual appearance of existing models without mesh redesign.

关键词： graphics processing unit

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