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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Cross-Modulated Few-Shot image Generation for Colorectal Tissue Classification 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Kumar, Amandeep Bhunia, Ankan Kumar Narayan, Sanath Cholakkal, Hisham Anwer, Rao Muhammad Laaksonen, Jorma Khan, Fahad Shahbaz MBZUAI Masdar City U Arab Emirates Aalto Univ Espoo Finland Technol Innovat Inst Masdar City U Arab Emirates Linkoping Univ Linkoping Sweden

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues. Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. Within our XM-GAN, a novel controllable fusion block densely aggregates local regions of reference images based on their similarity to those in the base image, resulting in locally consistent features. To the best of our knowledge, we are the first to investigate few-shot generation in colorectal tissue images. We evaluate our few-shot colorectral tissue image generation by performing extensive qualitative, quantitative and subject specialist (pathologist) based evaluations. Specifically, in specialist-based evaluation, pathologists could differentiate between our XM-GAN generated tissue images and real images only 55% time. Moreover, we utilize these generated images as data augmentation to address the few-shot tissue image classification task, achieving a gain of 4.4% in terms ofmean accuracy over the vanilla few-shot classifier. Code: https://***/VIROBO-15/XM-GAN.

关键词： Few-shot image generation Cross Modulation

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Reconstruction of medical images from Sparse Data: A Deep Learning Approach 5

Reconstruction of Medical Images from Sparse Data: A Deep Le...

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Saravanan, S. Bruntha, P.Malin Joseph S, Iwin thanakumar Subramanian, Suresh Sundar, G. Naveen Narmadha, D. VIT School of Design Vellore Institute of Technology Department of Multimedia Vellore India Karunya Institute of Technology and Sciences Department of Electronics and Communication Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of CSE Andhrapradesh Vaddeswaram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350395235

For diagnosis, medical imaging technology plays a significant role in today's scenario. Accordingly, enormous amounts of medical data are generated daily on research centers and hospitals. Storing the medical images needs a lot of space where in 3D (three dimensional) medical data acquires a huge space over it. All the medical image details need to be stored digitally for efficient diagnosis. the Digital Imaging and Communications in Medicine (DICOM) standard was established as the global norm for digitally storing patient images from diverse medical imaging modalities. Efficient compression over medical images is required to maintain efficacy in storage space. Compression over images like JPEG, PNG, etc. may not be efficient in bringing an effective lossless image compression in all the types of imaging modalities. Hence, an efficient methodology of image compression over medical images are required in today's scenario. this research study compares the performance of different methods of compression standard for medical images with the proposed method called DL Based Compressive sensing on medical image compression. the primary aim of Compressive Sensing (CS) is to achieve compression of a sparse signal at a sampling rate below the Nyquist rate. Results are analyzed using performance metrics like PSNR, MSE, BPP and CR. And it proves that the proposed algorithm achieves a higher PSNR value and states that it is efficient in bringing a lossless medical image compression. © 2024 IEEE.

关键词： image compression

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Construction of data domain based cascade ensemble learning algorithm for pathological image diagnosis 5

Construction of data domain based cascade ensemble learning ...

引用

5th international conference on computer Vision, image and Deep Learning, CVIDL 2024

作者： Hua, Jiajun Shanghai University College of Computer Engineering and Science Shanghai China

ISBN: (纸本)9798350373820

the use of medical images is an important link in the early detection of cancer. Artificial intelligence theories and technologies such as machine learning and neural networks have gradually become important tools to assist in solving the classification problems of pathological images. However, traditional methods ignore the irreversible cost caused by misdiagnosis when the tumor is classified as other types in the prediction process, and it is difficult to obtain a classifier with high robustness for all data sources due to the differences in the manipulations of different operators in the process of slicing, staining and extracting small images. In this paper, we construct a cascade ensemble learning algorithm BDD-Boost (Based on Data Domin-Boost). A pathological image classification method of cascading multiple classifiers is proposed and used. through the feature extraction and processing on the neurons of the preorder classifier, the adaptive threshold was generated to divide the data set. the postorder classifier was trained on the specified image set depending on the data set division result of the preorder classifier. Finally, the optimization results were obtained by integration. We verify the effectiveness of our algorithm by comparing experiments without BDD-Boost ensemble learning algorithm and with BDD-Boost ensemble learning algorithm. BDD-Boost improves the recognition performance by 1% on MobileNet_V2 and 2.2% on ResNet50. In the comparison of voting method and Bagging, it also shows better performance. the model proposed in this paper combines the advantages and characteristics of various convolutional neural networks, and has better performance and reliability than using a single convolutional neural network to directly predict the classification results of medical images under the condition of reducing computing resource consumption and calculation time. It can be used for computer-aided diagnosis and provide diagnostic reference for medical staff

关键词： image recognition

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Low-Dose CT image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication 26th

Low-Dose CT Image Super-Resolution Network with Dual-Guidanc...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Chi, Jianning Sun, Zhiyi Zhao, Tianli Wang, Huan Yu, Xiaosheng Wu, Chengdong Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis yet suffered from spatial resolution loss and artifacts. Numerous methods have been proposed to deal with those issues, but there still exists drawbacks: (1) convolution without guidance causes essential information not highlighted;(2) features with fixed-resolution lose the attention to multi-scale information;(3) single super-resolution module fails to balance details reconstruction and noise removal. therefore, we propose an LDCT image super-resolution network consisting of a dual-guidance feature distillation backbone for elaborate visual feature extraction, and a dual-path content communication head for artifacts-free and details-clear CT reconstruction. Specifically, the dual-guidance feature distillation backbone is composed of a dual-guidance fusion module (DGFM) and a sampling attention block (SAB). the DGFM guides the network to concentrate the feature representation of the 3D inter-slice information in the region of interest (ROI) by introducing the average CT image and segmentation mask as complements of the original LDCT input. Meanwhile, the elaborate SAB utilizes the essential multi-scale features to capture visual information more relative to edges. the dual-path reconstruction architecture introduces the denoising head before and after the super-resolution (SR) head in each path to suppress residual artifacts, respectively. Furthermore, the heads with the same function share the parameters so as to efficiently improve the reconstruction performance by reducing the amount of parameters. the experiments compared with 6 state-of-the-art methods on 2 public datasets prove the superiority of our method. the code is made available at https://***/neu- szy/dual-guidance_LDCT_SR.

关键词： Low-dose computed tomography image denoising image super-resolution Deep learning

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Detecting the Sensing Area of a Laparoscopic Probe in Minimally Invasive Cancer Surgery 26th

Detecting the Sensing Area of a Laparoscopic Probe in Minima...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Huang, Baoru Hu, Yicheng Nguyen, Anh Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Imperial Coll London Dept Surg & Canc London England Univ Liverpool Dept Comp Sci Liverpool Merseyside England

ISBN: (纸本)9783031439957;9783031439964

In surgical oncology, it is challenging for surgeons to identify lymph nodes and completely resect cancer even with pre-operative imaging systems like PET and CT, because of the lack of reliable intraoperative visualization tools. Endoscopic radio-guided cancer detection and resection has recently been evaluated whereby a novel tethered laparoscopic gamma detector is used to localize a preoperatively injected radiotracer. this can both enhance the endoscopic imaging and complement preoperative nuclear imaging data. However, gamma activity visualization is challenging to present to the operator because the probe is nonimaging and it does not visibly indicate the activity origination on the tissue surface. Initial failed attempts used segmentation or geometric methods, but led to the discovery that it could be resolved by leveraging high-dimensional image features and probe position information. To demonstrate the effectiveness of this solution, we designed and implemented a simple regression network that successfully addressed the problem. To further validate the proposed solution, we acquired and publicly released two datasets captured using a custom-designed, portable stereo laparoscope system. through intensive experimentation, we demonstrated that our method can successfully and effectively detect the sensing area, establishing a new performance benchmark. Code and data are available at https://***/br0202/Sensing area ***.

关键词： Laparoscopic image-guided intervention Minimally Invasive Surgery Detection of Sensing Area

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Groupwise image Registration with Atlas of Multiple Resolutions Refined at Test Phase 26th

Groupwise Image Registration with Atlas of Multiple Resoluti...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： He, Ziyi Mok, Tony C. W. Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China

ISBN: (纸本)9783031474248;9783031474255

Groupwise image registration (GIR) is a fundamental task that facilitates the simultaneous deformation of a group of subjects towards a specified or implicit center. Existing works mainly focus on either optimization-based methods that provide superb results but consume substantial time, or learning-based methods that are efficient but lack the flexibility to generalize across different domains and scales. To leverage the advantages of both methodologies, we present a robust method, Test-time Atlas adaptation for Groupwise registration (TAG), which generates a high-quality, group-specific atlas for groups of varying resolutions. Our method allows training at the test phase on target groups based on a learning-based GIR framework that bridges the gap between diverse groups. Besides the refinement of atlases at the original resolution, we propose additional modules to extend the scheme to groups of higher or lower resolutions at little cost. the method is evaluated on 3D brain MRI datasets to demonstrate its effectiveness. Evaluations of the registration accuracy and unbiasedness of atlases illustrate that TAG outperforms state-of-the-art benchmarks and maintains flexibility and robustness under a variety of scenarios.

关键词： Atlas Generation Test-time Adapation Multiscale Imaging

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Robust Segmentation via Topology Violation Detection and Feature Synthesis 26th

Robust Segmentation via Topology Violation Detection and Fea...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Li, Liu Ma, Qiang Ouyang, Cheng Li, Zeju Meng, Qingjie Zhang, Weitong Qiao, Mengyun Kyriakopoulou, Vanessa Hajnal, Joseph V. Rueckert, Daniel Kainz, Bernhard Imperial Coll London London England Univ Oxford Oxford England Kings Coll London London England Tech Univ Munich Munich Germany FAU Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9783031439001;9783031439018

Despite recent progress of deep learning-based medical image segmentation techniques, fully automatic results often fail to meet clinically acceptable accuracy, especially when topological constraints should be observed, e.g., closed surfaces. Although modern image segmentation methods show promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Union, these metrics do not reflect the correctness of a segmentation in terms of a required topological genus. Existing approaches estimate and constrain the topological structure via persistent homology (PH). However, these methods are not computationally efficient as calculating PH is not differentiable. To overcome this problem, we propose a novel approach for topological constraints based on the multi-scale Euler Characteristic (EC). To mitigate computational complexity, we propose a fast formulation for the EC that can inform the learning process of arbitrary segmentation networks via topological violation maps. Topological performance is further facilitated through a corrective convolutional network block. Our experiments on two datasets show that our method can significantly improve topological correctness.

关键词： image segmentation

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Masked Frequency Consistency for Domain-Adaptive Semantic Segmentation of Laparoscopic images 26th

Masked Frequency Consistency for Domain-Adaptive Semantic Se...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhao, Xinkai Hayashi, Yuichiro Oda, Masahiro Kitasaka, Takayuki Mori, Kensaku Nagoya Univ Grad Sch Informat Nagoya Aichi Japan Nagoya Univ Informat Strategy Off Informat & Commun Nagoya Aichi Japan Aichi Inst Technol Dept Informat Sci Toyota Japan Nagoya Univ Ctr Informat Technol Nagoya Aichi Japan Natl Inst Informat Res Ctr Med Bigdata Tokyo Japan

ISBN: (纸本)9783031439063;9783031439070

Semantic segmentation of laparoscopic images is an important issue for intraoperative guidance in laparoscopic surgery. However, acquiring and annotating laparoscopic datasets is labor-intensive, which limits the research on this topic. In this paper, we tackle the Domain-Adaptive Semantic Segmentation (DASS) task, which aims to train a segmentation network using only computer-generated simulated images and unlabeled real images. To bridge the large domain gap between generated and real images, we propose a Masked Frequency Consistency (MFC) module that encourages the network to learn frequency-related information of the target domain as additional cues for robust recognition. Specifically, MFC randomly masks some high-frequency information of the image to improve the consistency of the network's predictions for low-frequency images and real images. We conduct extensive experiments on existing DASS frameworks with our MFC module and show performance improvements. Our approach achieves comparable results to fully supervised learning method on the CholecSeg8K dataset without using any manual annotation. the code is available at ***/MoriLabNU/MFC.

关键词： Unsupervised Domain Adaptation Laparoscopic image Semantic Segmentation

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Enhancing medical Imaging: Denoising with Discrete Fourier Transform, Clustering, and Statistical Analysis 5

Enhancing Medical Imaging: Denoising with Discrete Fourier T...

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5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Nandini, Bandaru Jaya Sahithi, Mamidi Leha Harshika, Siddareddy Gari Jayan, Sarada Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Mathematics Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331528539

In the complex realm of medical imaging, noise poses a significant challenge as every pixel can convey crucial information that might save lives. Various factors, including environmental noise, sensor noise, and the intrinsic constraints of imaging equipment, can cause denoising in images. this study introduces a novel image denoising approach specifically applied to the Alzheimer's dataset, which is crucial for understanding and diagnosing Alzheimer's disease. the dataset comprises five distinct classes, each representing different stages or types of the disease, making it particularly significant in the medical field. theproposed method leverages a combination of Discrete Fourier Transform (DFT), Inverse Discrete Fourier Transform (IDFT), Butterworth low-pass filtering, and Non-local Means denoising to enhance image quality. By employing Fourier Transform, the study performs frequency domain analysis on the image data, enabling the extraction of vital information while reducing noise. Furthermore, K-means clustering is used to categorize noisy and denoised images based on visual similarities. To validate the effectiveness of the denoising technique, statistical evaluations are conducted through hypothesis testing, comparing noise levelsand quality metrics between noisy and denoised images. thisstudy not only offers a comprehensive approach to addressing the challenges of image denoising in medical imaging but alsohas significant societal benefits. By improving the clarity of images used in Alzheimer's research, the study contributes to more accurate diagnoses and better understanding of the disease, potentially leading to improved patient outcomes and advancing public health efforts in combating Alzheimer's disease. © 2024 IEEE.

关键词： Butterworth filters

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LoRA-MedSAM: Efficient medical image Segmentation 5th

LoRA-MedSAM: Efficient Medical Image Segmentation

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5th international conference on medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Hu, Jiamin Xu, Xuwei Zou, Zhenmin Department of Mechanical and Aerospace Engineering University of Manchester Manchester United Kingdom School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9789819638628

medical image segmentation becomes increasingly important for identifying and delineating anatomical structures, diseases, and abnormalities within medical images. However, existing large pre-trained foundation models for medical image segmentation, such as MedSAM, are often limited by their substantial computing resource requirements when fine-tuned for specific tasks. this high computational burden hinders their deployment in real-world clinical scenarios where rapid model adaptation is crucial yet computing resources are typically constrained. To address the challenge, in this paper, we introduce LoRA-MedSAM, a highly efficient method for fine-tuning large pre-trained models on medical image segmentation tasks. In particular, we leverage the Low-Rank Adaptation technique to significantly expedite the fine-tuning process of MedSAM model while maintaining high performance. Extensive experiments on three widely adopted medical image segmentation datasets have demonstrated both the effectiveness and efficiency of our method. Specifically, LoRA-MedSAM drastically reduces the fine-tuning time by 22.7%, 14.8% and 29.4% while outperforming the vanilla MedSAM by 1.1%, 5.5% and 0.5% higher Dice Similarity Coefficient (DSC) on the AMOS, BTCV and MultiTalent datasets, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： medical imaging

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