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arXiv 2024年

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman The Biomedical data analysis and visualization lab School of Computer Science The University of Sydney NSW Australia The Department of Molecular Imaging Royal Prince Alfred Hospital NSW Australia The Institute of Translational Medicine Shanghai Jiao Tong University Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hyungseob Shin Hyeongyu Kim Sewon Kim Yohan Jun Taejoon Eo Dosik Hwang School of Electrical and Electronic Engineering Naver AI Lab Naver Cloud Martinos Center for Biomedical Imaging Harvard Medical School Probe Medical Inc. Department of Oral and Maxillofacial Radiology College of Dentistry Department of Radiology and Center for Clinical Imaging Data Science College of Medicine @Yonsei University Center for Healthcare Robotics Korea Institute of Science and Technology

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for Slice-Direction Continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.

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MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

MGCT: Mutual-Guided Cross-Modality Transformer for Survival ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Mingxin Liu, Yunzan Cui, Hui Li, Chunquan Ma, Jiquan Heilongjiang University Department of Computer Science and Technology Harbin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia University of South China Institute of Cardiovascular Disease Hengyang Medical School The First Affiliated Hospital Hengyang China University of South China Cardiovascular Lab of Big Data and Imaging Artificial Intelligence Hengyang Medical School The First Affiliated Hospital Hengyang China University of South China School of Basic Medical Sciences Hengyang Medical School Department of Biochemistry and Molecular Biology Hengyang China

ISBN: (纸本)9798350337488

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000×150,000 pixels;(2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data;and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods. © 2023 IEEE.

关键词： Computational Pathology Multimodal Deep Learning Survival Prediction Weakly-Supervised Learning

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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III-V/Si hybrid external-cavity laser utilizing TM mode in the passive photonic circuit 27

III-V/Si hybrid external-cavity laser utilizing TM mode in t...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Guo, Yuyao Li, Xinhang Jin, Minhui Lu, Liangjun Xie, Jingya Chen, Jianping Zhou, Linjie Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center University of Shanghai for Science and Technology Shanghai Key Lab of Modern Optical System Shanghai200093 China

We present a III-V/Si hybrid laser using the silicon waveguide TM mode to reduce the propagation loss, backscattering, and two-photon absorption. The TM-mode laser has superior performance than the TE-mode laser. ... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Photons

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SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Shin, Hyungseob Kim, Hyeongyu Kim, Sewon Jun, Yohan Eo, Taejoon Hwang, Dosik School of Electrical and Electronic Engineering Yonsei University Korea Republic of Department of Oral and Maxillofacial Radiology College of Dentistry Yonsei University Korea Republic of Department of Radiology Center for Clinical Imaging Data Science College of Medicine Yonsei University Korea Republic of Naver AI Lab Naver Cloud Probe Medical Inc. United Kingdom Martinos Center for Biomedical Imaging United States Harvard Medical School United States Center for Healthcare Robotics Korea Institute of Science and Technology Korea Republic of

关键词： Medical imaging

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Reconstruction Of Quantitative Susceptibility Maps From Phase Of Susceptibility Weighted imaging With Cross-Connected Ψ-Net

Reconstruction Of Quantitative Susceptibility Maps From Phas...

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IEEE International Symposium on Biomedical imaging

作者： Zhiyang Lu Jun Li Zheng Li Hongjian He Jun Shi School of Communication and Information Engineering Shanghai University China Center for Brain Imaging Science and Technology Zhejiang University China Research Center for Healthcare Data Science Zhejiang Lab China

ISBN: (纸本)9781665412469;9781665429474

Quantitative Susceptibility Mapping (QSM) is a new phasebased technique for quantifying magnetic susceptibility. The existing QSM reconstruction methods generally require complicated pre-processing on high-quality phase data. In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected Ψ-Net (CΨ-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing. CΨ-Net adds an intermediate branch in the classical U-Net to form a Ψ-like structure. The specially designed dilated interaction block is embedded in each level of this branch to enlarge the receptive fields for capturing more susceptibility information from a wider spatial range of phase images. Moreover, the crossed connections are utilized between branches to implement a multi-resolution feature fusion scheme, which helps CΨ-Net capture rich contextual information for accurate reconstruction. The experimental results on a human dataset show that CΨ-Net achieves superior performance in our task over other QSM reconstruction algorithms.

关键词： Magnetic separation Reconstruction algorithms Task analysis Image reconstruction Magnetic susceptibility Biomedical imaging

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Automated Pipeline for Regional Epicardial Adipose Tissue Distribution Analysis in the Left Atrium 15th

Automated Pipeline for Regional Epicardial Adipose Tissue Di...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Lluch, Èric Castañeda, Eduardo Weiss, Maximilian E. R. Vizitiu, Anamaria Jacob, Athira Jami, Jitin Audigier, Chloé Meister, Felix Mihalef, Viorel Passerini, Tiziano Siemens Healthineers Digital Technologies and Innovation Erlangen Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Siemens SRL Brasov Romania Siemens Healthineers Digital Technology and Innovation Princeton United States Machine-Learning and Data Analytics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Advanced Clinical Imaging Technology Siemens Healthineers AG Lausanne Switzerland

ISBN: (纸本)9783031877551

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting approximately 3% of the global population and rising to 11% in individuals over 80 years old. The distribution of epicardial adipose tissue (EAT) around the atria plays a significant role in the pathophysiology of AF, yet its regional analysis remains underexplored. This paper introduces an automated pipeline capable of segmenting 14 atrial regions from patient CT angiograms, a notable advancement over previous attempts to automate the creation of synthetic anatomical or physics-based twins. Our fully automatic pipeline requires no human supervision and provides the regional EAT quantification successfully on 91.9% of the analyzed patients for which the LA and its structures were automatically segmented (1797 out of 1954 patients), including the automatic segmentation of the aorta and pericardium to assess EAT volume around the left atrium. By applying principal component analysis of the regional EAT to analyze a separate dataset of 84 individuals who were clinically labeled, we identified a distinct group that contained all the patients with AF. This finding suggests that there is a significant link between the pattern of fat distribution and AF. This novel approach has the potential to enhance our understanding of AF and improve patient outcomes by enabling large-scale, regional fat analysis in an automated manner on all patients with accurate segmentation of left atrial structures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Principal component analysis

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A Speech-to-Video Synthesis Approach Using Spatio-Temporal Diffusion for Vocal Tract MRI

arXiv

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arXiv 2025年

作者： Pérez-Toro, Paula Andrea Arias-Vergara, Tomás Xing, Fangxu Liu, Xiaofeng Stone, Maureen Zhuo, Jiachen Orozco-Arroyave, Juan Rafael Nöth, Elmar Hutter, Jana Prince, Jerry L. Maier, Andreas Woo, Jonghye Harvard Medical School Massachusetts General Hospital BostonMA02114 United States Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Bayern 91058 Germany GITA Lab Faculty of Engineering Universidad de Antioquia Antioquia Medellín050010 Colombia Department of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science Yale University New HeavenCT06510 United States Department of Neural and Pain Sciences Department of Orthodontics and Pediatrics University of Maryland School of Dentistry BaltimoreMD21210 United States Department of Orthodontics and Pediatrics University of Maryland School of Dentistry BaltimoreMD21201 United States Smart Imaging Lab Radiological Institute University Hospital Erlangen Bayern Erlangen91052 Germany Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States

Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treatment and rehabilitation strategies. Toward this goal, we introduce an audio-to-video generation framework for creating Real Time/cine-Magnetic Resonance imaging (RT-/cine-MRI) visuals of the vocal tract from speech signals. Our framework first preprocesses RT-/cine-MRI sequences and speech samples to achieve temporal alignment, ensuring synchronization between visual and audio data. We then employ a modified stable diffusion model, integrating structural and temporal blocks, to effectively capture movement characteristics and temporal dynamics in the synchronized data. This process enables the generation of MRI sequences from new speech inputs, improving the conversion of audio into visual data. We evaluated our framework on healthy controls and tongue cancer patients by analyzing and comparing the vocal tract movements in synthesized videos. Our framework demonstrated adaptability to new speech inputs and effective generalization. In addition, positive human evaluations confirmed its effectiveness, with realistic and accurate visualizations, suggesting its potential for outpatient therapy and personalized simulation of vocal tract visualizations. © 2025, CC BY-NC-ND.

关键词： Visualization

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

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

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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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