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Tissue concepts: Supervised foundation models in computational pathology

computers in Biology and Medicine 2025年 186卷 109621-109621页

作者： Nicke, Till Schäfer, Jan Raphael Höfener, Henning Feuerhake, Friedrich Merhof, Dorit Kießling, Fabian Lotz, Johannes Fraunhofer Institute for Digital Medicine MEVIS Bremen/Lübeck/Aachen Germany Institute of Image Analysis and Computer Vision University of Regensburg Regensburg Germany Institute for Pathology Hannover Medical School Hannover Germany Institute for Experimental Molecular Imaging RWTH Aachen University Aachen Germany Institute of Neuropathology Medical Center – University of Freiburg Freiburg Germany

Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Foundation models have the potential to improve generalizability within and across centers and serve as starting points for data efficient development of specialized yet robust AI models. However, the training of foundation models themselves is usually very expensive in terms of data, computation, and time. This paper proposes a supervised training method that drastically reduces these expenses. The proposed method is based on multi-task learning to train a joint encoder, by combining 16 different classification, segmentation, and detection tasks on a total of 912,000 patches. Since the encoder is capable of capturing the properties of the samples, we term it the Tissue Concepts encoder. To evaluate the performance and generalizability of the Tissue Concepts encoder across centers, classification of whole slide images from four of the most prevalent solid cancers – breast, colon, lung, and prostate – was used. The experiments show that the Tissue Concepts model achieve comparable performance to models trained with self-supervision, while requiring only 6% of the amount of training patches. Furthermore, the Tissue Concepts encoder outperforms an imageNet pre-trained encoder on both in-domain and out-of-domain data. The pre-trained models and will be made available under https://***/FraunhoferMEVIS/MedicalMultitaskModeling. © 2025 The Authors

关键词： Self-supervised learning

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image reconstruction based on human and monkey cone mosaics

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Journal of Visual Communication and image Representation 1990年第2期1卷 137-152页

作者： Costaridou, Lena Stefanou, Spyros Hirsch, Joy Orphanoudakis, Stelios Image Analysis and Computer Vision Laboratory Institute of Computer Science Foundation of Research and Technology-Hellas Heraklion Crete Greece Department of Ophthalmology and Visual Science Yale University New Haven CT United States Image Analysis and Computer Vision Laboratory Institute of Computer Science Foundation of Research and Technology-Hellas Heraklion Crete Greece Departments of Diagnostic Imaging and Electrical Engineering Yale University New Haven CT United States

The image reconstruction capacity of the actual foveal cone sampling mosaics of an adult monkey and human is investigated through computer simulations. A retinocortical mapping model in which positions are known and disorder is compensated (DC) has been implemented using classical Whittaker-Kotelnikov-Shannon (WKS) reconstruction theory and a two-dimensional hexagonal tessellation technique that maps the cone lattice onto a perfectly hexagonal grid. The DC model has been compared with a retinocortical mapping model in which cone positions are also known but position disorder is uncompensated (DU) in the reconstruction process. A control model has also been implemented using WKS reconstruction based on sampled data obtained with simulated perfectly hexagonal lattices whose Nyquist limits are equivalent to those of the human and monkey lattices. Quantitative comparisons of these models are based on the RMS error per pixel between the original and reconstructed images. Results of these simulations demonstrate a pronounced superiority of the DC over the DU model. The results of these simulations, which use "biologically correct" sampling lattices, document a potential image reconstruction advantage when foveal cone positions are known. © 1990.

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Contrast enhancement with dual energy CT for the assessment of atherosclerosis

Contrast enhancement with dual energy CT for the assessment ...

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2009 - Workshop on image Processing for Medicine: Algorithms - Systems - Applications, BVM 2009

作者： Saur, Stefan C. Alkadhi, Hatem Regazzoni, Luca Eugster, Simon Székely, Gábor Cattin, Philippe Computer Vision Laboratory ETH Zurich Switzerland Institute of Diagnostic Radiology University Hospital Zurich Switzerland Medical Image Analysis Center University of Basel Switzerland

ISBN: (纸本)9783540938590

A drawback of the commonly used single source computed tomography systems (CT) is that different materials might show very similar attenuation at any selected radiation energy. However, the assessment of atherosclerosis requires good differentiation between vessel lumen, calcium, adipose, and surrounding tissue. Dual energy CT (DECT) simultaneously measures attenuations at two energies and therefore can improve the differentiation to some extent. A tissue cancelation and enhancement algorithm for dual energy data was already proposed in 1981 and evaluated on experimental settings with a stationary X-ray source. For this study, we adapted this algorithm for DECT and propose its usage as a pre-processing step for the assessment of atherosclerosis. On clinical DECT patient data and with fixed parameters we could show a simultaneous contrast enhancement between 8% and 67% among all targeted tissues.

关键词： Tissue

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Towards Monocular Shape from Refraction 32

Towards Monocular Shape from Refraction

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32nd British Machine vision Conference, BMVC 2021

作者： Sulc, Antonin Sato, Imari Goldluecke, Bastian Treibitz, Tali Computer Vision and Image Analysis University of Konstanz Konstanz Germany National Institute of Informatics Tokyo Japan Charney School of Marine Sciences University of Haifa Haifa Israel

Refraction is a common physical phenomenon and has long been researched in computer vision. Objects imaged through a refractive object appear distorted in the image as a function of the shape of the interface between the media. This hinders many computer vision applications, but can be utilized for obtaining the geometry of the refractive interface. Previous approaches for refractive surface recovery largely relied on various priors or additional information like multiple images of the analyzed surface. In contrast, we claim that a simple energy function based on Snell's law enables the reconstruction of an arbitrary refractive surface geometry using just a single image and known background texture and geometry. In the case of a single point, Snell's law has two degrees of freedom, therefore to estimate a surface depth, we need additional information. We show that solving for an entire surface at once introduces implicit parameter-free spatial regularization and yields convincing results when an intelligent initial guess is provided. We demonstrate our approach through simulations and real-world experiments, where the reconstruction shows encouraging results in the single-frame monocular setting. © 2021. The copyright of this document resides with its authors.

关键词： Refraction

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Information Processing in Medical Imaging 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Gábor Székely Horst K. Hahn

ISBN: (数字)9783642220920

ISBN: (纸本)9783642220913

This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.

关键词： image Processing and computer vision Pattern Recognition Artificial Intelligence computer Graphics Health Informatics Imaging / Radiology

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Face recognition - A one-shot learning perspective 15

Face recognition - A one-shot learning perspective

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15th International Conference on Signal image Technology and Internet Based Systems, SISITS 2019

作者： Chanda, Sukalpa Gv, Asish Chakrapani Brun, Anders Hast, Anders Pal, Umapada Doermann, David Department of Information Technology Østfold University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Centre for Image Analysis Uppsala University Sweden Computer Science and Engineering University at Buffalo United States

ISBN: (纸本)9781728156866

Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems. © 2019 IEEE.

关键词： Face recognition

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Evaluation of fast 2D and 3D medical image retrieval approaches based on image miniatures

Evaluation of fast 2D and 3D medical image retrieval approac...

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2nd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2011

作者： Donner, René Haas, Sebastian Burner, Andreas Holzer, Markus Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University of Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)9783642284595

The present work evaluates four medical image retrieval approaches based on features derived from image miniatures. We argue that due to the restricted domain of medical image data, the standardized acquisition protocols and the absence of a potentially cluttered background a holistic image description is sufficient to capture high-level image similarities. We compare four different miniature 2D and 3D descriptors and corresponding metrics, in terms of their retrieval performance: (A) plain miniatures together with euclidean distances in a k Nearest Neighbor based retrieval backed by kD-trees;(B) correlations of rigidly aligned miniatures, initialized using the kD-tree;(C) distribution fields together with the l1-norm;(D) SIFT-like histogram of gradients using the χ2-distance. We evaluate the approaches on two data sets: the imageClef 2009 benchmark of 2D radiographs with the aim to categorize the images and a large set of 3D-CTs representing a realistic sample in a hospital PACS with the objective to estimate the location of the query volume. © 2012 Springer-Verlag.

关键词： Medical imaging

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Fast anatomical structure localization using top-down image patch regression

Fast anatomical structure localization using top-down image ...

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15th International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Donner, René Menze, Bjoern H. Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria CSAIL MIT Cambridge MA United States Asclepios Project INRIA Sophia-Antipolis France Computer Vision Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783642366192

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time. © 2013 Springer-Verlag.

关键词： computer aided diagnosis

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VISCERAL: Towards large data in medical imaging - Challenges and directions

VISCERAL: Towards large data in medical imaging - Challenges...

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3rd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2012

作者： Langs, Georg Hanbury, Allan Menze, Bjoern Müller, Henning Computational Image Analysis and Radiology Lab. Department of Radiology Medical University of Vienna Austria Institute for Software Technology and Interactive Systems Faculty of Computer Science Vienna University of Technology Austria Computer Vision Lab. ETH Zurich Switzerland Switzerland

ISBN: (纸本)9783642366772

The increasing amount of medical imaging data acquired in clinical practice holds a tremendous body of diagnostically relevant information. Only a small portion of these data are accessible during clinical routine or research due to the complexity, richness, high dimensionality and size of the data. There is consensus in the community that leaps in this regard are hampered by the lack of large bodies of data shared across research groups and an associated definition of joint challenges on which development can focus. In this paper we describe the objectives of the project Visceral. It will provide the means to jump-start this process by providing access to unprecedented amounts of real world imaging data annotated through experts and by using a community effort to generate a large corpus of automatically generated standard annotations. To this end, Visceral will conduct two competitions that tackle large scale medical image data analysis in the fields of anatomy detection, and content-based image retrieval, in this case the retrieval of similar medical cases using visual data and textual radiology reports. © 2013 Springer-Verlag.

关键词： Medical imaging

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Flowing crossfade of endoscopic images for the generation of a mosaic 1

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2005 - Workshop on image Processing for Medicine: Algorithms - Systems - Applications, BVM 2005

作者： Wald, Diana Reeff, Mireille Székely, Gabor Cattin, Philippe Paulus, Dietrich Medical Image Analysis and Visualization Group Computer Vision Laboratory Swiss Federal Institute of Technology Sternwartstr. 7 CH-8092 Zurich Switzerland Institut für Computervisualistik Universitätsstr. 1 D-56070 Koblenz Germany

ISBN: (数字)9783540264316

ISBN: (纸本)3540250522

Im Beitrag wird ein neues Verfahren zum fließenden Überblenden von Endoskopiebildern vorgestellt. Spezielles Augenmerk wird dabei auf die Erhaltung von feinsten Details in den Aufnahmen und einen guten visuellen Gesamteindruck gelegt. Die Bilder werden zunächst in ihrer Intensität angeglichen. Die Überlappungsbereiche werden anschließend mit dem neuen Verfahren überblendet.

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