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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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Scribble2D5: Weakly-Supervised Volumetric image Segmentation via Scribble Annotations 25th

Scribble2D5: Weakly-Supervised Volumetric Image Segmentation...

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

作者： Chen, Qiuhui Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9783031164521;9783031164514

image segmentation using weak annotations like scribbles has gained great attention, since such annotations are easier to obtain compared to time-consuming and labor-intensive labeling at the pixel/voxel level. However, scribbles lack structure information of the region of interest (ROI), thus existing scribble-based methods suffer from poor boundary localization. Moreover, current methods are mostly designed for 2D image segmentation, which do not fully leverage volumetric information. In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary predictions. To achieve this, we augment a 2.5D attention UNet with a proposed label propagation module to extend semantic information from scribbles and a combination of static and active boundary prediction to learn ROI's boundaries and regularize its shape. Extensive experiments on three public datasets demonstrate Scribble2D5 significantly outperforms existing scribble-based methods and approaches the performance of fully-supervised ones. Our code is available at https://***/Qybc/Scribble2D5.

关键词： Weakly-supervised learning Scribble annotation Volumetric image segmentation

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Lesion-Aware Contrastive Learning for Diabetic Retinopathy Diagnosis 26th

Lesion-Aware Contrastive Learning for Diabetic Retinopathy D...

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

作者： Cheng, Shuai Hou, Qingshan Cao, Peng Yang, Jinzhu Liu, Xiaoli Zaiane, Osmar R. Northeastern Univ Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Minist Educ Key Lab Intelligent Comp Med Image Shenyang Peoples R China Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang 110819 Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Alberta Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9783031439896;9783031439902

Early diagnosis and screening of diabetic retinopathy are critical in reducing the risk of vision loss in patients. However, in a real clinical situation, manual annotation of lesion regions in fundus images is time-consuming. Contrastive learning(CL) has recently shown its strong ability for self-supervised representation learning due to its ability of learning the invariant representation without any extra labelled data. In this study, we aim to investigate how CL can be applied to extract lesion features in medical images. However, can the direct introduction of CL into the deep learning framework enhance the representation ability of lesion characteristics? We show that the answer is no. Due to the lesion-specific regions being insignificant in medical images, directly introducing CL would inevitably lead to the effects of false negatives, limiting the ability of the discriminative representation learning. Essentially, two key issues should be considered: (1) How to construct positives and negatives to avoid the problem of false negatives? (2) How to exploit the hard negatives for promoting the representation quality of lesions? In this work, we present a lesion-aware CL framework for DR grading. Specifically, we design a new generating positives and negatives strategy to overcome the false negatives problem in fundus images. Furthermore, a dynamic hard negatives mining method based on knowledge distillation is proposed in order to improve the quality of the learned embeddings. Extensive experimental results show that our method significantly advances state-of-the-art DR grading methods to a considerable 88.0%ACC/86.8% Kappa on the EyePACS benchmark dataset. Our code is available at https://***/IntelliDAL/image.

关键词： Diabetic Retinopathy Contrastive Learning Hard Negative Mining Knowledge Distillation

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Carbon Footprint of Selecting and Training Deep Learning Models for medical image Analysis 25th

Carbon Footprint of Selecting and Training Deep Learning Mod...

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

作者： Selvan, Raghavendra Bhagwat, Nikhil Anthony, Lasse F. Wolff Kanding, Benjamin Dam, Erik B. Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Neurosci Copenhagen Denmark McGill Univ Montreal PQ Canada

ISBN: (纸本)9783031164439;9783031164422

the increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon footprint of developing DL models for medical image analysis (MIA), where volumetric images of high spatial resolution are handled. We present and compare the features of four tools from literature to quantify the carbon footprint of DL. Using one of these tools we estimate the carbon footprint of medical image segmentation pipelines. We choose nnU-net as the proxy for a medical image segmentation pipeline and experiment on three common datasets. With our work we hope to inform on the increasing energy costs incurred by MIA. We discuss simple strategies to cut-down the environmental impact that can make model selection and training processes more efficient.

关键词： Energy consumption Carbon emissions image segmentation Deep learning

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image based Reversible Data Hiding using Multiple image Layer 5

Image based Reversible Data Hiding using Multiple Image Laye...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Rana, Pratham Singh Kalpana Chahat Singh, Bhupinder Singla, Sanjay Kaur, Charnpreet Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350321425

Reversible Data Hiding (RDH) is a widely used method to embed data into digital images while preserving the integrity of the original image. the idea behind RDH is to allow decoding of embedded data while the cover image remains unchanged. this technique is particularly useful in applications where the original image must be preserved, such as medical image processing and authentication. the main objective of RDH is to maximize the delivered power and at the same time minimize the distortion due to the delivery process. Different embedding techniques have been proposed to achieve high embedding power and low distortion. these techniques include difference expansion, histogram transformation, and prediction error expansion. these methods change the pixel values of the cover image to accommodate the hidden data. the extracted key is then used to restore the original pixel values, allowing the original image to be restored. this paper proposes a new RDH algorithm that achieves high embedding power with low distortion and high security. the proposed algorithm uses a combination of image layers, encryption and histogram shift to embed the data. the suggested technique shows promising results in terms of embedding power, image quality, and computing complexity while maintaining a low amount of distortion and low computational cost, making it appropriate for real-time applications and promising results in terms of embedding power, image quality and computational complexity. the future direction of RDH research is to explore new immersive technologies and applications in various fields such as security, forensics and multimedia communication © 2023 IEEE.

关键词： Restoration

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An Efficient 3-D Model for Early Prediction of Breast Cancer Based on Hybrid ANN - Fuzzy Model through Lossless medical MR images 5

An Efficient 3-D Model for Early Prediction of Breast Cancer...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Renukadevi, P. Lakkshmanan, Ajanthaa Brindha, R. Jeyakumar, D. Srm Institute of Science and Technology Dept of Computing Technologies Kattankalathur India Dhanalakshmi College of Engineering Dept of Computer Science and Engg Chennai India

ISBN: (纸本)9798350321425

Rapid advancements in technology has aided in early prediction of Breast cancer which is a high mortality rate characterized condition. Fuzzy and Neural network-based models have been effective in prediction of early occurrences of breast cancer. Any method now in use must be able to detect breast cancer in women at an early stage to effectively bring down the mortality rate. the major goal of the suggested approach is to apply 3-D picture lossless compression in NN to determine the tissue size from an MRI breast scanning image. the use of a 3-D predictor to categorise the photos and the application of the NN approach to determine the degree of breast cancer accuracy are the primary features of this work. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Mitral Regurgitation Quantification from Multi-channel Ultrasound images via Deep Learning 26th

Mitral Regurgitation Quantification from Multi-channel Ultra...

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

作者： Tang, Keming Ge, Zhenyi Ling, Rongbo Cheng, Jun Xue, Wufeng Pan, Cuizhen Shu, Xianhong Ni, Dong Shenzhen Univ Med Sch Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUS Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Fudan Univ Dept Echocardiog Shanghai Inst Cardiovasc Dis Zhongshan Hosp Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Mitral regurgitation (MR) is the most common heart valve disease. Prolonged regurgitation can cause changes in the heart size, lead to impaired systolic and diastolic capacity, and even threaten life. In clinical practice, MR is evaluated by the proximal isovelocity surface area (PISA) method, where manual measurements of the regurgitation velocity and the value of PISA radius from multiple ultrasound images are required to obtain the mitral regurgitant stroke volume (MRSV) and effective regurgitant orifice area (EROA). In this paper, we propose a fully automatic method for MR quantification, which follows the pipeline of ECG-based cycle detection, Doppler spectrum segmentation, PISA radius segmentation, and MR quantification. Specifically, for the Doppler spectrum segmentation, we proposed a novel adaptive-weighting multi-channel segmentation network, PISA-net, to accurately identify the upper and lower contours of the PISA radius from a pair of coupled M-mode PISA image and corresponding M-mode decolored image. Using the complementary information of the two coupled images and combing with the spatial attention module, the proposed PISA-net can well identify the contours of the PISA radius and therefore lead to accurate quantification of MR parameters. To the best of our knowledge, this is the first study of automatic MR quantification. Experimental results demonstrated the effectiveness of the whole pipeline, especially the PISA-net for PISA radius segmentation. the full method achieves a high Pearson correlation of 0.994 for both MRSV and EROA, implying its great potential in the clinical application of MR diagnosis.

关键词： Mitral regurgitation Multi-channel Segmentation

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Overlooked Trustworthiness of Saliency Maps 1

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

作者： Zhang, Jiajin Chao, Hanqing Dasegowda, Giridhar Wang, Ge Kalra, Mannudeep K. Yan, Pingkun Rensselaer Polytech Inst Dept Biomed Engn Troy NY USA Rensselaer Polytech Inst Ctr Biotechnol & Interdisciplinary Studies Troy NY USA Harvard Med Sch Massachusetts Gen Hosp Dept Radiol Boston MA USA

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Various saliency visualization methods have been proposed to explain artificial intelligence (AI) models towards building the trustworthiness of AI-driven medical image computing applications. However, an important question has yet to be answered - are the saliency maps themselves trustworthy? the trustworthiness of saliency methods has largely been overlooked. this paper first proposes the criteria and methods to evaluate the trustworthiness of saliency maps. then, a series of systematic studies are performed on a large-scale dataset with a commonly adopted deep neural network. the results show that: (i) Saliency maps may not be relevant to the model outputs;(ii) Saliency maps lack resistance and can be tampered without changing the model output. By demonstrating these risks of the current saliency methods, we suggest the community using saliency maps with caution when explaining AI models. Our source code is available at https://***/DIAL-RPI/Trustworthiness-of-medical-XAI.

关键词： Explainability Saliency maps Trustworthiness medical image analysis Deep learning

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Super-Resolution of Diffusion-Weighted images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. these two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. the results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

the proceedings contain 102 papers. the special focus in this conference is on Cognitive computing and Cyber Physical Systems. the topics include: Comparative Analysis Between Fuzzy theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor Networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and image Processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using medical images;non-invasive Technique for Detecting Glycosuria through image Processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;thz Wideband Metamaterial Absorber for Different Applications.

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Efficient Population Based Hyperparameter Scheduling for medical image Segmentation 25th

Efficient Population Based Hyperparameter Scheduling for Med...

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

作者： He, Yufan Yang, Dong Myronenko, Andriy Xu, Daguang NVidia Santa Clara CA 95051 USA

ISBN: (纸本)9783031164439;9783031164422

the training hyperparameters (learning rate, augmentation policies, e.t.c) are key factors affecting the performance of deep networks for medical image segmentation. Manual or automatic hyperparameter optimization (HPO) is used to improve the performance. However, manual tuning is infeasible for a large number of parameters, and existing automatic HPO methods like Bayesian optimization are extremely time consuming. Moreover, they can only find a fixed set of hyperparameters. Population based training (PBT) has shown its ability to find dynamic hyperparameters and has fast search speed by using parallel training processes. However, it is still expensive for large 3D medical image datasets with limited GPUs, and the performance lower bound is unknown. In this paper, we focus on improving the network performance using hyperparameter scheduling via PBT with limited computation cost. the core idea is to train the network with a default setting from prior knowledge, and finetune using PBT based hyperparameter scheduling. Our method can achieve 1%-3% performance improvements over default setting while only taking 3%-10% computation cost of training from scratch using PBT.

关键词： Hyperparameter optimization Population based training Bayesian optimization medical image segmentation

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