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Transformer-Based End-to-End Classification of Variable-Length Volumetric Data 26th

Transformer-Based End-to-End Classification of Variable-Leng...

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

作者： Oghbaie, Marzieh Araujo, Teresa Emre, Taha Schmidt-Erfurth, Ursula Bogunovic, Hrvoje Med Univ Vienna Dept Ophthalmol & Optometry Christian Doppler Lab Artificial Intelligence Ret Vienna Austria Med Univ Vienna Dept Ophthalmol & Optometry Lab Ophthalm Image Anal Vienna Austria

ISBN: (纸本)9783031439865;9783031439872

the automatic classification of 3D medical data is memoryintensive. Also, variations in the number of slices between samples is common. Naive solutions such as subsampling can solve these problems, but at the cost of potentially eliminating relevant diagnosis information. Transformers have shown promising performance for sequential data analysis. However, their application for long sequences is data, computationally, and memory demanding. In this paper, we propose an end-to-end Transformer-based framework that allows to classify volumetric data of variable length in an efficient fashion. Particularly, by randomizing the input volume-wise resolution(#slices) during training, we enhance the capacity of the learnable positional embedding assigned to each volume slice. Consequently, the accumulated positional information in each positional embedding can be generalized to the neighbouring slices, even for high-resolution volumes at the test time. By doing so, the model will be more robust to variable volume length and amenable to different computational budgets. We evaluated the proposed approach in retinal OCT volume classification and achieved 21.96% average improvement in balanced accuracy on a 9-class diagnostic task, compared to state-of-the-art video transformers. Our findings show that varying the volume-wise resolution of the input during training results in more informative volume representation as compared to training with fixed number of slices per volume.

关键词： Optical coherence tomography 3D volume classification Transformers

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Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection 26th

Smooth Attention for Deep Multiple Instance Learning: Applic...

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

作者： Wu, Yunan Castro-Macias, Francisco M. Morales-Alvarez, Pablo Molina, Rafael Katsaggelosl, Aggelos K. Northwestern Univ Dept Elect & Comp Engn Image & Video Proc Lab Evanston IL USA Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Granada Dept Stat & Operat Res Granada Spain Univ Granada Res Ctr Informat & Commun Technol CITIC UGR Granada Spain

ISBN: (纸本)9783031439032;9783031439049

Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assumes that instances in each bag are independent samples from a given distribution. However, instances are often spatially or sequentially ordered, and one would expect similar diagnostic importance for neighboring instances. To address this, in this study, we propose a smooth attention deep MIL (SA-DMIL) model. Smoothness is achieved by the introduction of first and second order constraints on the latent function encoding the attention paid to each instance in a bag. the method is applied to the detection of intracranial hemorrhage (ICH) on head CT scans. the results show that this novel SA-DMIL: (a) achieves better performance than the non-smooth attention MIL at both scan (bag) and slice (instance) levels;(b) learns spatial dependencies between slices;and (c) outperforms current state-of-the-art MIL methods on the same ICH test set.

关键词： Smooth attention Multiple instance learning CT hemorrhage diagnosis

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Semi-supervised Pathological image Segmentation via Cross Distillation of Multiple Attentions 26th

Semi-supervised Pathological Image Segmentation via Cross Di...

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

作者： Zhong, Lanfeng Liao, Xin Zhang, Shaoting Wang, Guotai Univ Elect Sci & Technol China Chengdu Peoples R China Sichuan Univ West China Univ Hosp 2 Dept Pathol Chengdu Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Segmentation of pathological images is a crucial step for accurate cancer diagnosis. However, acquiring dense annotations of such images for training is labor-intensive and time-consuming. To address this issue, Semi-Supervised Learning (SSL) has the potential for reducing the annotation cost, but it is challenged by a large number of unlabeled training images. In this paper, we propose a novel SSL method based on Cross Distillation of Multiple Attentions (CDMA) to effectively leverage unlabeled images. Firstly, we propose a Multi-attention Tri-branch Network (MTNet) that consists of an encoder and a three-branch decoder, with each branch using a different attention mechanism that calibrates features in different aspects to generate diverse outputs. Secondly, we introduce Cross Decoder Knowledge Distillation (CDKD) between the three decoder branches, allowing them to learn from each other's soft labels to mitigate the negative impact of incorrect pseudo labels in training. Additionally, uncertainty minimization is applied to the average prediction of the three branches, which further regularizes predictions on unlabeled images and encourages inter-branch consistency. Our proposed CDMA was compared with eight state-of-the-art SSL methods on the public DigestPath dataset, and the experimental results showed that our method outperforms the other approaches under different annotation ratios. the code is available at https://***/HiLab-git/CDMA.

关键词： Semi-supervised learning Knowledge distillation Attention Uncertainty

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Evaluating the Effectiveness of Feature Selection and Explainable AI in Predicting Acute Myocardial Infarction Using Machine Learning Models 5

Evaluating the Effectiveness of Feature Selection and Explai...

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5th international conference on Machine Learning and computer Application, ICMLCA 2024

作者： Wang, Yong Shen, Jiexuan Li, Shi Li, Xinglin Nneji, Grace Ugochi Monday, Happy Nkanta Chengdu University of Technology Chengdu610059 China HACE SOFTTECH Intelligent Computing Lab Lagos102241 Nigeria

ISBN: (纸本)9798331530334

Acute myocardial Infarctions (MIs), commonly known as heart attacks, are critical medical emergencies that often lead to severe complications, including post-myocardial infarction angina (PMIA). Early and accurate prediction of PMIA is crucial for timely intervention and improved patient outcomes. this study focuses on predicting PMIA using machine learning classifiers on the UCI myocardial infarction complication datasets, which includes 1700 patient samples with 111 clinical features. the dataset is balanced using Borderline Synthetic Minority Oversampling (Borderline-SMOTE) and standardized. Twelve classifiers are employed and the Shapley Additive exPlanations (SHAP) are used to interpret the best-performing models, revealing the decision-making processes. the SVM classifier achieved the highest cross-validated accuracy of 98.55%, demonstrating superior predictive capability. SHAP values provided insights into the importance of each feature, enhancing model interpretability and transparency. this approach highlights the significance of feature selection and explain ability in building trustworthy AI models for healthcare. the findings contribute to improved diagnosis and management of PMIA, underscoring the ethical adoption of AI in clinical settings. © 2024 IEEE.

关键词： Decision making

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Full image-Index Remainder Based Single Low-Dose DR/CT Self-supervised Denoising 26th

Full Image-Index Remainder Based Single Low-Dose DR/CT Self-...

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

作者： Long, Yifei Pan, Jiayi Xi, Yan Zhang, Jianjia Wu, Weiwen Sun Yat sen Univ Sch Biomed Engn Shenzhen Campus Shenzhen 518107 Peoples R China Shanghai First Imaging Informat Technol Co LTD Shanghai Peoples R China

ISBN: (纸本)9783031439896;9783031439902

Low-dose digital radiography (DR) and computed tomography (CT) play a crucial role in minimizing health risks during clinical examinations and diagnoses. However, reducing the radiation dose often leads to lower signal-to-noise ratio measurements, resulting in degraded image quality. Existing supervised and self-supervised reconstruction techniques have been developed with noisy and clean image pairs or noisy and noisy image pairs, implying they cannot be adapted to single DR and CT image denoising. In this study, we introduce the Full image-Index Remainder (FIRE) method. Our method begins by dividing the entire high-dimensional image space into multiple low-dimensional sub-image spaces using a full image-index remainder technique. By leveraging the data redundancy present within these sub-image spaces, we identify similar groups of noisy sub-images for training a self-supervised denoising network. Additionally, we establish a sub-space sampling theory specifically designed for self-supervised denoising networks. Finally, we propose a novel regularization optimization function that effectively reduces the disparity between self-supervised and supervised denoising networks, thereby enhancing denoising training. through comprehensive quantitative and qualitative experiments conducted on both clinical low-dose CT and DR datasets, we demonstrate the remarkable effectiveness and advantages of our FIRE method compared to other state-of-the-art approaches.

关键词： Digital radiography Computed tomography Self-supervised Remainder image denoising

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Distributionally Robust image Classifiers for Stroke Diagnosis in Accelerated MRI 26th

Distributionally Robust Image Classifiers for Stroke Diagnos...

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

作者： Hao, Boran Shen, Guoyao Chen, Ruidi Farris, Chad W. Anderson, Stephan W. Zhang, Xin Paschalidis, Ioannis Ch. Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Boston Univ Dept Mech Engn Boston MA 02215 USA Boston Univ Photon Ctr Boston MA 02215 USA Boston Med Ctr Boston MA 02118 USA Boston Univ Chobanian & Avedisian Sch Med Boston MA 02118 USA

ISBN: (纸本)9783031439032;9783031439049

Magnetic Resonance Imaging (MRI) acceleration techniques using k-space sub-sampling (KS) can greatly improve the efficiency of MRI-based stroke diagnosis. Although Deep Neural Networks (DNN) have shown great potential on stroke lesion recognition tasks when the MR images are reconstructed from the full k-space, they are vulnerable to the lower quality MR images generated by KS. In this paper, we propose a Distributionally Robust Learning (DRL) approach to improve the performance of stroke recognition DNN models when the MR images are reconstructed from the sub-sampled k-space. For Convolutional Neural Network (CNN) and Vision Transformer (ViT)-based models, our methods improve the stroke classification AUROC and AUPRC by up to 11.91% and 9.32% on the KS-perturbed brain MR images, respectively, compared against Empirical Risk Minimization (ERM) and other baseline defensive methods. We further show that DRL models can successfully recognize the stroke cases from highly perturbed MR images where clinicians may fail, which provides a solution for improved diagnosis in an accelerated MRI setting.

关键词： Stroke diagnosis MRI acceleration Distributionally robust optimization Deep learning

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Deep Learning for medical image Analysis 5th

Deep Learning for Medical Image Analysis

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5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Panda, Saswat Parida, Sahul Kumar Khatri, Ripusudan Kaur, Rupinder Chandigarh Univ Dept Comp Sci Ludhiana Punjab India

ISBN: (纸本)9789819720811;9789819720828

medical image analysis aims to enhance medical outcomes by using images of internal body parts for purposes such as research and diagnosis. the field has been transformed by deep learning, which has achieved remarkable results in tasks such as image alignment, partitioning, feature extraction, and categorization. the main factor for this shift is the advances in computing power and the emergence of deep neural networks. Deep learning is adept at finding hidden patterns in medical images and helps doctors make more precise diagnoses. they have shown their usefulness in tasks such as organ categorization, cancer detection, disease categorization, and computer-aided diagnosis. Various deep learning methods have been developed to analyze medical images for different diagnoses. In this article, we review the application of state-of-the-art deep learning techniques in medical image processing. We first survey the clinical trials that use neural networks. We then review popular pre-training models and discuss different integration methods that can enhance the performance of the neural network. Finally, we measure the performance indicators for the deep learning model and present the results.

关键词： medical images Diagnoses Network Cancer

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Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despeckling 26th

Content-Preserving Diffusion Model for Unsupervised AS-OCT I...

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

作者： Li, Sanqian Higashita, Risa Fu, Huazhu Li, Heng Niu, Jingxuan Liu, Jiang Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Tomey Corp Nagoya Aichi Japan Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9783031439896;9783031439902

Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging technique that is highly valuable for ophthalmic diagnosis. However, speckles in AS-OCT images can often degrade the image quality and affect clinical analysis. As a result, removing speckles in AS-OCT images can greatly benefit automatic ophthalmology analysis. Unfortunately, challenges still exist in deploying effective AS-OCT image denoising algorithms, including collecting sufficient paired training data and the requirement to preserve consistent content in medical images. To address these practical issues, we propose an unsupervised AS-OCT despeckling algorithm via Content Preserving Diffusion Model (CPDM) with statistical knowledge. At the training stage, a Markov chain transforms clean images to white Gaussian noise by repeatedly adding random noise and removes the predicted noise in a reverse procedure. At the inference stage, we first analyze the statistical distribution of speckles and convert it into a Gaussian distribution, aiming to match the fast truncated reverse diffusion process. We then explore the posterior distribution of observed images as a fidelity term to ensure content consistency in the iterative procedure. Our experimental results show that CPDM significantly improves image quality compared to competitive methods. Furthermore, we validate the benefits of CPDM for subsequent clinical analysis, including ciliary muscle (CM) segmentation and scleral spur (SS) localization.

关键词： ASOCT Unsupervised despeckling Diffusion model

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HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide image Analysis 26th

HIGT: Hierarchical Interaction Graph-Transformer for Whole S...

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

作者： Guo, Ziyu Zhao, Weiqin Wang, Shujun Yu, Lequan Univ Hong Kong Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9783031439865;9783031439872

In computation pathology, the pyramid structure of gigapixel Whole Slide images (WSIs) has recently been studied for capturing various information from individual cell interactions to tissue microenvironments. this hierarchical structure is believed to be beneficial for cancer diagnosis and prognosis tasks. However, most previous hierarchical WSI analysis works (1) only characterize local or global correlations within the WSI pyramids and (2) use only unidirectional interaction between different resolutions, leading to an incomplete picture of WSI pyramids. To this end, this paper presents a novel Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With Graph Neural Network and Transformer as the building commons, HIGT can learn both short-range local information and long-range global representation of the WSI pyramids. Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids. Finally, we aggregate both coarse-grained and fine-grained features learned from different levels together for slide-level prediction. We evaluate our methods on two public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and esophageal carcinoma (ESCA). Experimental results show that our HIGT outperforms both hierarchical and non-hierarchical state-of-the-art methods on both tumor subtyping and staging tasks.

关键词： WSI analysis Hierarchical representation Interaction Graph neural network Vision transformer

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Adversarial Attack and Defense Mechanisms in medical Imaging: A Comprehensive Review 5

Adversarial Attack and Defense Mechanisms in Medical Imaging...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Surekha, M. Sagar, Anil Kumar Khemchandani, Vineeta Sharda University Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

medical imaging is essential in modern healthcare, allowing for accurate diagnoses and treatment planning. the practice of machine learning algorithms into clinical imaging uses has significantly enhanced diagnostic abilities. In contrast, as technology has forward-looking, new problems have emerged, main among them existence adversarial assaults. this complete research study investigates the background information of adversarial attacks and its rising defense measures on medical image model. Adversarial assaults involve intelligent alterations of input data to change machine learning algorithms, possibly important to misdiagnosis or inaccurate clinical diagnosis. this paper thoroughly analyses many adversarial assault practices precisely tailored for medical imaging based on division, classification and rebuilding of clinical image with different defensive tactic. Also, the research study provides the better path for collect rising counter measures on adversarial attacks posed by threats. these protective measures contain different types of proposals. For example, adversarial practices, input pre-processing and algorithm understandable strategies. © 2024 IEEE.

关键词： Adversarial attack adversarial defense evaluation medical image analysis taxonomy

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