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Diffusion Deformable Model for 4D Temporal medical image Generation 25th

Diffusion Deformable Model for 4D Temporal Medical Image Gen...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Kim, Boah Ye, Jong Chul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9783031164316;9783031164309

Temporal volume images with 3D-ht (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively studied, approaches for temporal medical image generation such as 4D cardiac volume data are limited. In this work, we present a novel deep learning model that generates intermediate temporal volumes between source and target volumes. Specifically, we propose a diffusion deformable model (DDM) by adapting the denoising diffusion probabilistic model that has recently been widely investigated for realistic image generation. Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path. Once our model is trained, the latent code estimated from the diffusion module is simply interpolated and fed into the deformation module, which enables DDM to generate temporal frames along the continuous trajectory while preserving the topology of the source image. We demonstrate the proposed method with the 4D cardiac MR image generation between the diastolic and systolic phases for each subject. Compared to the existing deformation methods, our DDM achieves high performance on temporal volume generation.

关键词： Deep learning medical image generation image deformation Diffusion model

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Domain Adaptation Using Generative Adversarial Networks for medical image Synthesis 5th

Domain Adaptation Using Generative Adversarial Networks for ...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ganesh, D. Verma, Upendra K. Patil, Smita Alam, Intekhab Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India 3School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Latest advancements in state-of-the-art scientific photograph evaluation depend heavily on the availability of large new datasets of cutting-edge annotated scientific photographs for version training. Unfortunately, obtaining massive samples of cutting-edge categorized scientific picas is ultra-modern high-priced, or impossible. Area edition is a way to generalize a version educated on a selected dataset to a brand new area, wherein the model turned into not directly educated. Generative adverse Networks (GANs) were used to generate realistic artificial photographs. It has enabled the opportunity present day the usage of GANs to bridge the domain hole via generating artificial medical photos, which could then be used for education fashions. On this paper;we evaluate the latest progress in making use of area edition the use of GANs for scientific photosynthesis, with an emphasis on applications including pathology, scientific imaging, and optical acceptable imaging. We talk about the benefits of ultra-modern using GANs for area adaptation and present processes for each present day of the above programs. We also speak about various challenges and future directions in this field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A Novel Approach to Enhance Effectiveness of image Segmentation Techniques on Extremely Noisy medical images 5th

A Novel Approach to Enhance Effectiveness of Image Segmentat...

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5th international conference on Recent Trends in image Processing and Pattern Recognition (RTIP2R)

作者： Deshpande, Anuja LAD Coll Dept Elect Seminary Hills Campus Nagpur 440006 Maharashtra India

ISBN: (纸本)9783031235986;9783031235993

through this study, I contribute towards segmentation of liver areas and have proposed additional improvements, which positively influence image segmentation. In this study, I have subjected medical images from LiTS - Liver Tumour Segmentation Challenge, which are extremely noisy, to various image segmentation techniques belonging to fully automatic and semi-automatic categories. these varied techniques implement different approaches towards image segmentation problem. All the techniques had initially failed to segment the images with very poor results. Commonly used filters for pre-processing, such as median filter, top hat filter, wiener filter, etc., were ineffective in reducing the noise effectively. through this study, I have introduced a new combinatorial approach which not only is easier to implement but also much faster as well and resulted in much more enhanced input image quality that significantly improved the segmentation outcomes. Our approach has reduced noise, sharpened the edges, "localized" the segmentation problem before subjecting to various segmentation techniques. the techniques which had failed previously now could segment the images with improved speed of execution, efficiency and accuracy. I have studied our approach on 10 well known image segmentation techniques. Accuracy of these segmentation techniques was determined by computing Jaccard Index, Dice Coefficient and Hausdorff Distance.

关键词： Noise removal Segmentation accuracy image filtering Effectiveness De-convolution

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RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level image Quality Assessment 25th

RTN: Reinforced Transformer Network for Coronary CT Angiogra...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Lu, Yiting Fu, Jun Li, Xin Zhou, Wei Liu, Sen Zhang, Xinxin wu, Wei Jia, Congfu Liu, Ying Chen, Zhibo Univ Sci & Technol China Hefei Anhui Peoples R China Dalian Med Univ Affiliated Hosp 1 Dalian Liaoning Peoples R China

ISBN: (纸本)9783031164316;9783031164309

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. the appropriate CCTA Vessel-level image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. the primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL module (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. there are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on endto-end optimization. the experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.

关键词： image quality assessment CCTA Reinforced learning Transformer

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Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide images Analysis 25th

Lesion-Aware Contrastive Representation Learning for Histopa...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Li, Jun Zheng, Yushan Wu, Kun Shi, Jun Xie, Fengying Jiang, Zhiguo Beihang Univ Sch Astronaut Image Proc Ctr Beijing 102206 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Engn Med Beijing 100191 Peoples R China Hefei Univ Technol Sch Software Hefei 230601 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

image representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. the previous representation learning methods followed the supervised learning paradigm. However, manual annotation for large-scale WSIs is time-consuming and labor-intensive. Hence, the self-supervised contrastive learning has recently attracted intensive attention. In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis. We built a lesion queue based on the memory bank structure to store the representations of different classes of WSIs, which allowed the contrastive model to selectively define the negative pairs during the training. Moreover, We designed a queue refinement strategy to purify the representations stored in the lesion queue. the experimental results demonstrate that LACL achieves the best performance in histopathology image representation learning on different datasets, and outperforms state-of-the-art methods under different WSI classification benchmarks.

关键词： Weakly supervised learning Contrastive learning WSI analysis

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An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided Attention 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Xia, Chao Wang, Jiyue Qin, Yulei Gu, Yun Chen, Bing Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Inst Hematol Ruijin Hosp Shanghai Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. Existing computer-aided chromosome recognition methods mainly focus on better feature representation of individual chromosomes while neglecting the fact that chromosomes from the same karyotype share some common distribution and are more related compared to chromosomes across different patients. In the light of such observation, we start from a global perspective and propose an end-to-end differential combinatorial optimization method for R-band chromosome recognition. To achieve this, a grouping guided feature interaction module (GFIM) is built for feature aggregation between similar chromosome instances. Specially, a mask matrix is built for self-attention computation according to chromosome length grouping information. Furthermore, a deep assignment module (DAM) is designed for flexible and differentiable label assignment. It exploits the aggregated features to infer class probability distributions between all chromosomes in a cell. Experimental results on both normal and numerically abnormal karyotypes confirmed that our method outperforms state-of-the-art chromosome recognition and label assignment methods. the code is available at: https://***/xiabc612/R-band-chromosome-recognition.

关键词： R-band chromosome Label assignment Transformer

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UASSR: Unsupervised Arbitrary Scale Super-Resolution Reconstruction of Single Anisotropic 3D images via Disentangled Representation Learning 25th

UASSR: Unsupervised Arbitrary Scale Super-Resolution Reconst...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wang, Jiale Wang, Runze Tao, Rong Zheng, Guoyan Shanghai Jiao Tong Univ Inst Med Robot Sch Biomed Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Deep learning-based single image super resolution (SISR) algorithms have great potential to recover high-resolution (HR) images from low-resolution (LR) inputs. However, most studies require paired LR and HR images for a supervised training, which are difficult to organize in clinical applications. In this paper, we propose an unsupervised arbitrary scale super-resolution reconstruction (UASSR) method based on disentangled representation learning, eliminating the requirement of paired images for training. Applying our method to applications of generating HR images with high inter-plane resolution from LR images with low inter-plane resolution. At the inference stage, we design a strategy to fuse multiple reconstructed HR images from different views to achieve better super-resolution (SR) result. We conduct experiments on one publicly available dataset including 507 MR volumes of the knee joint and an in-house dataset containing 130 CT volumes of the lower spine. Results from our comprehensive experiments demonstrate superior performance of UASSR over other state-of-the-art methods. A reference implementation of our method can be found at: https://***/jialewang1/UASSR.

关键词： Unsupervised image super-resolution Arbitrary scale Disentangled representation learning Magnetic resonance imaging Computed tomography

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the Dice Loss in the Context of Missing or Empty Labels: Introducing Φ and ε 25th

The Dice Loss in the Context of Missing or Empty Labels: Int...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Tilborghs, Sofie Bertels, Jeroen Robben, David Vandermeulen, Dirk Maes, Frederik Katholieke Univ Leuven ESAT PSI Dept Elect Engn Leuven Belgium UZ Leuven Med Imaging Res Ctr Leuven Belgium Icometrix Kolonel Begaultlaan Ib-12 Leuven Belgium

ISBN: (纸本)9783031164439;9783031164422

Albeit the Dice loss is one of the dominant loss functions in medical image segmentation, most research omits a closer look at its derivative, i.e. the real motor of the optimization when using gradient descent. In this paper, we highlight the peculiar action of the Dice loss in the presence of missing or empty labels. First, we formulate a theoretical basis that gives a general description of the Dice loss and its derivative. It turns out that the choice of the reduction dimensions Phi and the smoothing term epsilon is non-trivial and greatly influences its behavior. We find and propose heuristic combinations of Phi and e that work in a segmentation setting with either missing or empty labels. Second, we empirically validate these findings in a binary and multiclass segmentation setting using two publicly available datasets. We confirm that the choice of Phi and is indeed pivotal. With Phi chosen such that the reductions happen over a single batch (and class) element and with a negligible epsilon, the Dice loss deals with missing labels naturally and performs similarly compared to recent adaptations specific for missing labels. With Phi chosen such that the reductions happen over multiple batch elements or with a heuristic value for epsilon, the Dice loss handles empty labels correctly. We believe that this work highlights some essential perspectives and hope that it encourages researchers to better describe their exact implementation of the Dice loss in future work.

关键词： image segmentation

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Transformer Based Multiple Instance Learning for Weakly Supervised Histopathology image Segmentation 25th

Transformer Based Multiple Instance Learning for Weakly Supe...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Qian, Ziniu Li, Kailu Lai, Maode Chang, Eric I-Chao Wei, Bingzheng Fan, Yubo Xu, Yan Beihang Univ State Key Lab Software Dev Environm Key Lab Biomechan Mechanobiol Minist Educ Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Beijing 100191 Peoples R China China Pharmaceut Univ Nanjing 210009 Peoples R China Zhejiang Univ Hangzhou 310058 Peoples R China Microsoft Res Beijing 100080 Peoples R China Xiaomi Corp Beijing 100085 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. the development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation that it is time-consuming and labor-intensive. As a subset of weakly supervised learning, Multiple Instance Learning (MIL) has been proven to be effective in segmentation. However, there is a lack of related information between instances in MIL, which limits the further improvement of segmentation performance. In this paper, we propose a novel weakly supervised method for pixel-level segmentation in histopathology images, which introduces Transformer into the MIL framework to capture global or long-range dependencies. the multi-head self-attention in the Transformer establishes the relationship between instances, which solves the shortcoming that instances are independent of each other in MIL. In addition, deep supervision is introduced to overcome the limitation of annotations in weakly supervised methods and make the better utilization of hierarchical information. the state-of-the-art results on the colon cancer dataset demonstrate the superiority of the proposed method compared with other weakly supervised methods. It is worth believing that there is a potential of our approach for various applications in medical images.

关键词： Weakly supervised learning Transformer Multiple Instance Learning Segmentation

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Reinforcement Learning Driven Intra-modal and Inter-modal Representation Learning for 3D medical image Classification 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhu, Zhonghang Wang, Liansheng Magnier, Baptiste Zhu, Lei Zhang, Defu Yu, Lequan Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Montpellier IMT Mines Ales Euromov Digital Hlth Motion Ales France Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Multi-modality 3D medical images play an important role in the clinical practice. Due to the effectiveness of exploring the complementary information among different modalities, multi-modality learning has attracted increased attention recently, which can be realized by Deep Learning (DL) models. However, it remains a challenging task for two reasons. First, the prediction confidence of multi-modality learning network cannot be guaranteed when the model is trained with weakly-supervised volume-level labels. Second, it is difficult to effectively exploit the complementary information across modalities and also preserve the modality-specific properties when fusion. In this paper, we present a novel Reinforcement Learning (RL) driven approach to comprehensively address these challenges, where two Recurrent Neural Networks (RNN) based agents are utilized to choose reliable and informative features within modality (intra-learning) and explore complementary representations across modalities (inter-learning) with the guidance of dynamic weights. these agents are trained via Proximal Policy Optimization (PPO) with the confidence increment of the prediction as the reward. We take the 3D image classification as an example and conduct experiments on a multi-modality brain tumor MRI data. Our approach outperforms other methods when employing the proposed RL-based multi-modality representation learning.

关键词： Multi-modality learning 3D medical images Reinforcement learning Classification

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