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HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

Computers in Biology and Medicine 2025年 190卷 109991-109991页

作者： Ahmad, Zahoor Al-Thelaya, Khaled Alzubaidi, Mahmood Joad, Faaiz Gilal, Nauman Ullah Mifsud, William Boughorbel, Sabri Pintore, Giovanni Gobbetti, Enrico Schneider, Jens Agus, Marco Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Kite Pharma Santa MonicaCA United States Visual and Data-intensive Computing CRS4 Italy Qatar Computing Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images. © 2025 The Authors

关键词： Image annotation

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SPIDER: SPherical Indoor DEpth Renderer 9

SPIDER: SPherical Indoor DEpth Renderer

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9th Smart Tools and Applications in Graphics Conference, STAG 2022

作者： Tukur, M. Pintore, G. Gobbetti, E. Schneider, J. Agus, M. College of Science and Engineering HBKU Qatar Visual and Data-intensive Computing CRS4 Italy

ISBN: (纸本)9783038681915

Today’s Extended Reality (XR) applications that call for specific Diminished Reality (DR) strategies to hide specific classes of objects are increasingly using 360◦ cameras, which can capture entire areas in a single picture. In this work, we present an interactive-based image editing and rendering system named SPIDER, that takes a spherical 360◦ indoor scene as input. The system incorporates the output of deep learning models to abstract the segmentation and depth images of full and empty rooms to allow users to perform interactive exploration and basic editing operations on the reconstructed indoor scene, namely: i) rendering of the scene in various modalities (point cloud, polygonal, wireframe) ii) refurnishing (transferring portions of rooms) iii) deferred shading through the usage of precomputed normal maps. These kinds of scene editing and manipulations can be used for assessing the inference from deep learning models and enable several Mixed Reality (XR) applications in areas such as furniture retails, interior designs, and real estates. Moreover, it can also be useful in data augmentation, arts, designs, and paintings. © 2022 The Author(s)

关键词： Mixed reality

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Deep panoramic depth prediction and completion for indoor scenes

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Computational visual Media 2024年第5期10卷 903-922页

作者： Giovanni Pintore Eva Almansa Armando Sanchez Giorgio Vassena Enrico Gobbetti Visual and Data-intensive Computing CRS4Cagliari 09134Italy Gexcel srl Elmas(CA)09097Italy Department of Civil EnvironmentArchitectural Engineeringand Mathematics(DICATAM)Universita degli Studi di Brescia(UNIBS)Brescia(BS)25123Italy.

We introduce a novel end-to-end deeplearning solution for rapidly estimating a dense spherical depth map of an indoor *** input is a single equirectangular image registered with a sparse depth map,as provided by a variety of common capture *** is inferred by an efficient and lightweight single-branch network,which employs a dynamic gating system to process together dense visual data and sparse geometric *** exploit the characteristics of typical man-made environments to efficiently compress multiresolution features and find short-and long-range relations among scene ***,we introduce a new augmentation strategy to make the model robust to different types of sparsity,including those generated by various structured light sensors and LiDAR *** experimental results demonstrate that our method provides interactive performance and outperforms stateof-the-art solutions in computational efficiency,adaptivity to variable depth sparsity patterns,and prediction accuracy for challenging indoor data,even when trained solely on synthetic data without any fine tuning.

关键词： machine learning image processing and computervision visionand scene understanding 3D stereo scene analysis

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Automatic 3D modeling and exploration of indoor structures from panoramic imagery 24

Automatic 3D modeling and exploration of indoor structures f...

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2024 SIGGRAPH Asia 2024 Courses, SA Courses 2024

作者： Gobbetti, Enrico Pintore, Giovanni Agus, Marco National Research Center in High-Performance Computing Big Data and Quantum Computing Cagliari Italy Visual and Data-intensive Computing Center for Advanced Studies Research and Development in Sardinia Cagliari Italy College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798400711350

Surround-view panoramic imaging delivers extensive spatial coverage and is widely supported by professional and commodity capture devices. Research on inferring and exploring 3D indoor models from 360° images has recently flourished, resulting in highly effective solutions. Nevertheless, challenges persist due to the complexity and variability of indoor environments and issues with noisy and incomplete data. This course provides an up-to-date integrative view of the field. After introducing a characterization of input sources, we define the structure of output models, the priors exploited to bridge the gap between imperfect input and desired output, and the main characteristics of geometry reasoning and data-driven approaches. We then identify and discuss the main sub-problems in indoor reconstruction from panoramas and review and analyze state-of-the-art solutions for indoor capture, room modeling, integrated model computation, visual representation generation, and immersive exploration. Relevant examples of implemented pipelines are described, focusing on deep-learning solutions. We finally point out relevant research issues and analyze research trends. © 2024 Copyright held by the owner/author(s).

关键词： 3D reconstruction

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HistoContours: a framework for visual annotation of histopathology whole slide images

HistoContours: a framework for visual annotation of histopat...

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2022 Eurographics Workshop on visual computing for Biology and Medicine, EG VCBM 2022

作者： Al-Thelaya, K. Joad, F. Gilal, N.U. Mifsud, W. Pintore, G. Gobbetti, E. Agus, M. Schneider, J. Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar SIDRA Qatar Foundation Qatar Visual and Data-intensive Computing CRS4 Italy

ISBN: (纸本)9783038681779

We present an end-to-end framework for histopathological analysis of whole slide images (WSIs). Our framework uses deep learning-based localization & classification of cell nuclei followed by spatial data aggregation to propagate classes of sparsely distributed nuclei across the entire slide. We use YOLO ("You Only Look Once") for localization instead of more costly segmentation approaches and show that using HistAuGAN boosts its performance. YOLO finds bounding boxes around nuclei at good accuracy, but the classification accuracy can be improved by other methods. To this end, we extract patches around nuclei from the WSI and consider models from the SqueezeNet, ResNet, and EfficientNet families for classification. Where we do not achieve a clear separation between highest and second-highest softmax activation of the classifier, we use YOLO's output as a secondary vote. The result is a sparse annotation of the WSI, which we turn dense by using kernel density estimation. The result is a full vector of per pixel probabilities for each class of nucleus we consider. This allows us to visualize our results using both color-coding and isocontouring, reducing visual clutter. Our novel nuclei-to-tissue coupling allows histopathologists to work at both the nucleus and the tissue level, a feature appreciated by domain experts in a qualitative user study. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Tissue

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MultiPanoWise: holistic deep architecture for multi-task dense prediction from a single panoramic image

MultiPanoWise: holistic deep architecture for multi-task den...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Uzair Shah Muhammad Tukur Mahmood Alzubaidi Giovanni Pintore Enrico Gobbetti Mowafa Househ Jens Schneider Marco Agus ICT Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Visual and Data-Intensive Computing CRS4 Italy National Research Center in HPC Big Data and Quantum Computing Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a novel holistic deep-learning approach for multi-task learning from a single indoor panoramic image. Our framework, named MultiPanoWise, extends vision transformers to jointly infer multiple pixel-wise signals, such as depth, normals, and semantic segmentation, as well as signals from intrinsic decomposition, such as reflectance and shading. Our solution leverages a specific architecture combining a transformer-based encoder-decoder with multiple heads, by introducing, in particular, a novel context adjustment approach, to enforce knowledge distillation between the various signals. Moreover, at training time we introduce a hybrid loss scalarization method based on an augmented Chebychev/hypervolume scheme. We illustrate the capabilities of the proposed architecture on public-domain synthetic and real-world datasets. We demonstrate performance improvements with respect to the most recent methods specifically designed for single tasks, like, for example, individual depth estimation or semantic segmentation. To our knowledge, this is the first architecture capable of achieving state-of-the-art performance on the joint extraction of heterogeneous signals from single indoor omnidirectional images.

关键词： Training Reflectivity Computer vision Semantic segmentation Estimation Computer architecture Multitasking

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Spider: A Framework for Processing, Editing and Presenting Immersive High-Resolution Spherical Indoor Scenes

SSRN

引用

SSRN 2023年

作者： Agus, Marco Tukur, Muhammad Pintore, Giovanni Gobbetti, Enrico Schneider, Jens College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Visual and Data-intensive Computing CRS4 Italy

Today's Extended Reality (XR) applications that call for specific Diminished Reality (DR) strategies to hide specific classes of objects are increasingly using 360$^{\circ}$ cameras, which capture entire areas in a single picture. We present an interactive-based image processing, editing and rendering system named \textbf{SPIDER}, that takes a spherical 360$^{\circ}$ indoor scene as input. The system is composed of a novel integrated deep learning architecture for extracting geometric and semantic information of full and empty rooms, based on gated and dilated convolutions, followed by a super-resolution *** obtained high resolution representations allow users to perform interactive exploration and basic editing operations on the reconstructed indoor scene, like rendering of the scene in various modalities and refurnishing. These manipulations enable several Mixed Reality applications in areas such as interior designs,and real estates. We report on the performance improvement of the various processing components on public domain spherical image indoor datasets. © 2023, The Authors. All rights reserved.

关键词： Spheres

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Virtual Staging Technologies for the Metaverse

Virtual Staging Technologies for the Metaverse

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Muhammad Tukur Yehia Boraey Sara Jashari Alberto Jaspe Villanueva Uzair Shah Mahmood Al-Zubaidi Giovanni Pintore Enrico Gobbetti Jens Schneider Marco Agus Noora Fetais College of Science and Engineering HBKU Doha Qatar Department of Computer Science Qatar University Doha Qatar VCCVIS High Perf. Visualization KAUST Jeddah Saudi Arabia Visual and Data-intensive Computing CRS4 Cagliari Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

We discuss virtual staging technologies, focusing on two primary pipelines for creating and exploring immersive indoor environments in the metavers: an AI-based image processing pipeline and a LIDAR-based pipeline. The AI-based image processing pipeline leverages advanced AI algorithms for tasks such as clutter removal, semantic style transfer, and super-resolution, enabling rapid generation of high-quality, photorealistic virtual environments from single panoramic images. The LIDAR-based pipeline captures measurable 3D models of indoor spaces, facilitating immersive editing and collaborative design through real-time interaction with high-fidelity virtual environments. A qualitative comparative analysis of these technologies highlights their strengths and limitations in various applications. The practical implications of these pipelines are discussed, particularly their potential to transform industries such as real estate, furniture retail, interior design, construction, remote collaboration, and immersive training. The paper concludes with suggestions for future research, including conducting user studies, integrating the two pipelines, and optimizing technologies for mobile and edge devices to enhance accessibility and usability.

关键词： Training Solid modeling Three-dimensional displays Image processing Pipelines Superresolution Semantics Collaboration Transforms Usability

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