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TISSUE CONCEPTS: SUPERVISED FOUNDATION MODELS IN COMPUTATIONAL PATHOLOGY

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arXiv 2024年

作者： Nicke, Till Schäfer, Jan Raphael Höfener, Henning Feuerhake, Friedrich Merhof, Dorit Kießling, Fabian Lotz, Johannes Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Fraunhofer Institute for Digital Medicine MEVIS Aachen Germany Institute for Pathology Hannover Medical School Hannover Germany Institute of Image Analysis and Computer Vision University of Regensburg Regensburg Germany Institute for Experimental Molecular Imaging RWTH Aachen University Aachen 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 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. © 2024, CC BY-NC-ND.

关键词： Multi-task learning

DAE-Former: Dual Attention-guided Efficient Transformer for Medical image Segmentation

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arXiv 2022年

作者： Azad, Reza Arimond, René Aghdam, Ehsan Khodapanah Kazerouni, Amirhossein Merhof, Dorit Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Electrical Engineering Shahid Beheshti University Tehran Iran School of Electrical Engineering Iran University of Science and Technology Tehran Iran Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights. The code is publicly available at GitHub. © 2022, CC BY.

关键词： Reusability

Enhancing Medical image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

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arXiv 2023年

作者： Azad, Reza Jia, Yiwei Aghdam, Ehsan Khodapanah Cohen-Adad, Julien Merhof, Dorit The Institute of Imaging and Computer Vision RWTH Aachen University Aachen52074 Germany The Department of Electrical Engineering Shahid Beheshti University Tehran*** Iran The Mila Quebec AI Institute Canada The Functional Neuroimaging Unit CRIUGM University of Montreal Canada The NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal MontrealH3T 1J4 Canada The Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany The Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available on GitHub. © 2023, CC BY.

关键词： Merging

Implicit Neural Representation in Medical Imaging: A Comparative Survey

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arXiv 2023年

作者： Molaei, Amirali Aminimehr, Amirhossein Tavakoli, Armin Kazerouni, Amirhossein Azad, Bobby Azad, Reza Merhof, Dorit School of Computer Engineering Iran University of Science and Technology Tehran Iran School of Electrical Engineering Iran University of Science and Technology Tehran Iran South Dakota State University BrookingsSD United States Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multimodal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on GitHub. Finally, we aim to consistently incorporate the most recent and relevant papers regularly. © 2023, CC BY.

关键词： Medical imaging

Sampling possible reconstructions of undersampled acquisitions in MR imaging with a deep learned prior

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arXiv 2020年

作者： Tezcan, Kerem Can Karani, Neerav Baumgartner, Christian F. Konukoglu, Ender Computer Vision Lab ETH Zürich Switzerland Medical Image Analysis Group University of Tübingen Germany

Undersampling the k-space during MR acquisitions saves time, however results in an ill-posed inversion problem, leading to an infinite set of images as possible solutions. Traditionally, this is tackled as a reconstruction problem by searching for a single"best" image out of this solution set according to some chosen regularization or prior. This approach, however, misses the possibility of other solutions and hence ignores the uncertainty in the inversion process. In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process. To this end, we introduce a low dimensional latent space and model the posterior distribution of the latent vectors given the acquisition data in k-space, from which we can sample in the latent space and obtain the corresponding images. We use a variational autoencoder for the latent model and the Metropolis adjusted Langevin algorithm for the sampling. We evaluate our method on two datasets;with images from the Human Connectome Project and in-house measured multicoil images. We compare to five alternative methods. Results indicate that the proposed method produces images that match the measured k-space data better than the alternatives, while showing realistic structural variability. Furthermore, in contrast to the compared methods, the proposed method yields higher uncertainty in the undersampled phase encoding direction, as expected. © 2020, CC BY.

关键词： image reconstruction

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

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RecycleNet: Latent Feature Recycling Leads to Iterative Deci...

IEEE Workshop on Applications of computer vision (WACV)

作者： Gregor Koehler Tassilo Wald Constantin Ulrich David Zimmerer Paul F. Jaeger Jörg K. H. Franke Simon Kohl Fabian Isensee Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Helmholtz Imaging DKFZ National Center for Tumor Diseases (NCT) NCT Heidelberg a Partnership Between DKFZ University Medical Center Heidelberg Interactive Machine Learning Group DKFZ Machine Learning Lab University of Freiburg Freiburg Germany Latent Labs (***) London UK Applied Computer Vision Lab DKFZ Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

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arXiv 2023年

作者： Koehler, Gregor Wald, Tassilo Ulrich, Constantin Zimmerer, David Jaeger, Paul F. Franke, Jörg K.H. Kohl, Simon Isensee, Fabian Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Helmholtz Imaging DKFZ Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Interactive Machine Learning Group DKFZ Applied Computer Vision Lab DKFZ Machine Learning Lab University of Freiburg Freiburg Germany London United Kingdom Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

关键词： Network layers

One-Shot Learning-Based Handwritten Word Recognition 5th

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One-Shot Learning-Based Handwritten Word Recognition

5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Chakrapani Gv, Asish Chanda, Sukalpa Pal, Umapada Doermann, David Electronics and Communication Department Manipal University Jaipur Jaipur India Centre for Image Analysis Department of Information Technology Uppsala University Uppsala Sweden Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India University at Buffalo SUNY Buffalo United States

ISBN: (纸本)9783030412982

One-Shot and Few-shot Learning algorithms have emerged as techniques that can imitate a humans ability to learn from very few examples. This is an advantage over traditional deep networks which require a lot of training samples and lack of robustness due to their excessive domain specific discriminators. In this paper, we explore a one-shot learning approach to recognizing handwritten words using Siamese networks to classify the handwritten images at the word level. The Siamese network’s ability to compute similarities between two images is learned using a supervised metric but the fully trained Siamese network can be used to classify new data that has previously not been used to train the network. The model learns to discriminate inputs from a small labelled support set. By using a convolutional architecture we were able to achieve robust results. We also expect that training the system over a larger distributions of data will result in improved general handwritten word classification. Accuracy as high as 92.4% was obtained while performing 5-way one-shot word recognition on a publicly available dataset which is quite high in comparison to the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： image classification

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

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arXiv 2023年

作者： Jiao, Yiping van der Laak, Jeroen Albarqouni, Shadi Li, Zhang Tan, Tao Bhalerao, Abhir Ma, Jiabo Sun, Jiamei Pocock, Johnathan Pluim, Josien P.W. Koohbanani, Navid Alemi Bashir, Raja Muhammad Saad Raza, Shan E Ahmed Liu, Sibo Graham, Simon Wetstein, Suzanne Khurram, Syed Ali Watson, Thomas Rajpoot, Nasir Veta, Mitko Ciompi, Francesco Department of Pathology Radboud University Medical Center Nijmegen Netherlands School of Artificial Intelligence Nanjing University of Information Science Technology China Center for Medical Image Science and Visualization Linköping University Linköping Sweden Helmholtz AI Helmholtz Zentrum München Nuerherberg 85764 Germany Faculty of Informatics Technical University of Munich Garching85748 Germany College of Aerospace Science and Engineering National University of Defense Technology China Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation China Macao Polytechnic University China Department of Computer Science University of Warwick Coventry United Kingdom Huazhong University of Science and Technology China Singapore Medical Image Analysis Group Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Clinical Dentistry University of Sheffield Sheffield United Kingdom United Kingdom

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in histopathological images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon;we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform. LYSTO has supported a number of research in lymphocyte assessment in oncology. LYSTO will be a long-lasting educational challenge for deep learning and digital pathology, it is available at https://***/.MSC Codes 68T07 © 2023, CC BY.

关键词： Pathology