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Deep Multimodal Guidance for medical image Classification 25th

Deep Multimodal Guidance for Medical Image Classification

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

作者： Mallya, Mayur Hamarneh, Ghassan Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031164491;9783031164484

medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-feasible yet under-performing (inferior) modality and steer it towards improved performance. We focus on the application of deep learning for image-based diagnosis. We develop a light-weight guidance model that leverages the latent representation learned from the superior modality, when training a model that consumes only the inferior modality. We examine the advantages of our method in the context of two clinical applications: multi-task skin lesion classification from clinical and dermoscopic images and brain tumor classification from multi-sequence magnetic resonance imaging (MRI) and histopathology images. For both these scenarios we show a boost in diagnostic performance of the inferior modality without requiring the superior modality. Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference. We make our code and trained models available at: https://***/mayurmallya/DeepGuide.

关键词： Deep learning Multimodal learning Classification Student-teacher learning Knowledge distillation Skin lesions Brain tumors

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GradMix for Nuclei Segmentation and Classification in Imbalanced Pathology image Datasets 25th

GradMix for Nuclei Segmentation and Classification in Imbala...

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

作者： Doan, Tan Nhu Nhat Kim, Kyungeun Song, Boram Kwak, Jin Tae Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Sungkyunkwan Univ Kangbuk Samsung Hosp Dept Pathol Sch Med Seoul 05505 South Korea Korea Univ Sch Elect Engn Seoul 02841 South Korea

ISBN: (纸本)9783031164347;9783031164330

An automated segmentation and classification of nuclei is an essential task in digital pathology. the current deep learning-based approaches require a vast amount of annotated datasets by pathologists. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. In this paper, we propose a simple but effective data augmentation technique, termed GradMix, that is specifically designed for nuclei segmentation and classification. GradMix takes a pair of a major-class nucleus and a rare-class nucleus, creates a customized mixing mask, and combines them using the mask to generate a new rare-class nucleus. As it combines two nuclei, GradMix considers both nuclei and the neighboring environment by using the customized mixing mask. this allows us to generate realistic rare-class nuclei with varying environments. We employed two datasets to evaluate the effectiveness of GradMix. the experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets.

关键词： Nuclei segmentation and classification Data augmentation Data imbalance

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Learning-Based and Unrolled Motion-Compensated Reconstruction for Cardiac MR CINE Imaging 25th

Learning-Based and Unrolled Motion-Compensated Reconstructio...

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

作者： Pan, Jiazhen Rueckert, Daniel Kuestner, thomas Hammernik, Kerstin Tech Univ Munich Klinikum Rechts Isar Munich Germany Imperial Coll London Dept Comp London England Univ Hosp Tubingen Med Image & Data Anal MIDAS Lab Tubingen Germany

ISBN: (纸本)9783031164460;9783031164453

Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled subproblems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion. In this work, we propose a learning-based self-supervised framework for MCMR, to efficiently deal with nonrigid motion corruption in cardiac MR imaging. Contrary to conventional MCMR methods in which the motion is estimated prior to reconstruction and remains unchanged during the iterative optimization process, we introduce a dynamic motion estimation process and embed it into the unrolled optimization. We establish a cardiac motion estimation network that leverages temporal information via a group-wise registration approach, and carry out a joint optimization between the motion estimation and reconstruction. Experiments on 40 acquired 2D cardiac MR CINE datasets demonstrate that the proposed unrolled MCMR framework can reconstruct high quality MR images at high acceleration rates where other state-of-the-art methods fail. We also show that the joint optimization mechanism is mutually beneficial for both sub-tasks, i.e., motion estimation and image reconstruction, especially when the MR image is highly undersampled.

关键词： Motion estimation

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Heterogeneous Face Recognition Algorithm: Convolution Neural Network Approach 5

Heterogeneous Face Recognition Algorithm: Convolution Neural...

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5th international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickam, M. Ani Minisha, R. Rekha, K.S. Indumathy, T. Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rr and Dr Sr Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India Department of Medical Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350385649

Heterogeneous face recognition, also known as HFR, is an approach to facial recognition that attempts to match facial pictures that have been gathered through a variety of sensory modalities. HFR is primarily used for forensics, network security, and all commercial applications. the heterogeneous face recognition (HFR) algorithm is robust in handling challenges that exist in conventional face recognition. this is since heterogeneous face images display an enormous amount of intra- class variation, and it does not support hybrid modality of face images. this study provides a unique technique to transfer learning as a means of mastering invariant facial feature characteristics in visual (VIS) face pictures. A wrapping procedure combines end to end connections of two components for deep network. A discriminator has the details of fine-grained data for improving the quality of image which depends on wavelets. the system is made up of two different modules: one deals with face recognition, while the other determines a person's gender. It analysis about the characteristics of gender from the image using Convolutional Neural Networks that have been pre-trained. To train CNN in the face recognition module, in particular, the use of the open-source datasets Labelled Faces within the Wild, YouTube Face, and VGGFace2 results in an improvement in accuracy. Deep learning techniques and a substantial number of photographic examples of people's faces are what make it feasible for the proposed recognition to function so admirably. the problem of studying recognition remains challenging due to the limited size of the sample and the information gap between the senses. © 2024 IEEE.

关键词： Face recognition

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vMFNet: Compositionality Meets Domain-Generalised Segmentation 25th

vMFNet: Compositionality Meets Domain-Generalised Segmentati...

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

作者： Liu, Xiao thermos, Spyridon Sanchez, Pedro O'Neil, Alison Q. Tsaftaris, Sotirios A. Univ Edinburgh Sch Engn Edinburgh EH9 3FB Midlothian Scotland AC Codewheel Ltd Larnax Cyprus Alan Turing Inst London England Canon Med Res Europe Ltd Edinburgh Midlothian Scotland

ISBN: (纸本)9783031164491;9783031164484

Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from medical (e.g. MRI and CT) images with some limited guidance. Such recognition ability can easily generalise to new images from different clinical centres. this rapid and generalisable learning ability is mostly due to the compositional structure of image patterns in the human brain, which is less incorporated in medical image segmentation. In this paper, we model the compositional components (i.e. patterns) of human anatomy as learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected from different domains (e.g. clinical centres). the image features can be decomposed to (or composed by) the components with the composing operations, i.e. the vMF likelihoods. the vMF likelihoods tell how likely each anatomical part is at each position of the image. Hence, the segmentation mask can be predicted based on the vMF likelihoods. Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image. Extensive experiments show that the proposed vMFNet achieves improved generalisation performance on two benchmarks, especially when annotations are limited. Code is publicly available at: https://***/vios-s/vMFNet.

关键词： Compositionality Domain generalisation Semi-supervised learning Test-time training medical image segmentation

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Swin-MMC: Swin-Based Model for Myopic Maculopathy Classification in Fundus images 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

作者： Lu, Li Pan, Xuhao Jin, Panji Ding, Ye School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China School of Statistics and Information Shanghai University of International Business and Economics Shanghai201620 China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic maculopathy is a highly myopic retinal disorder that often occurs in highly myopic patients, serving as a major cause of visual impairment and blindness in numerous countries. Currently, fundus images serve as a prevalent diagnostic tool for myopic maculopathy. However, its efficacy relies on the expertise of clinicians, making the process labor-intensive. thus, we propose a model specifically designed for the image classification of myopic maculopathy, named Swin-MMC, based on the Swin Transformer model architecture, which achieves outstanding performance on the test dataset. To achieve a finer-grained classification of myopic maculopathy in fundus images, we have innovatively and for the first time proposed the use of enhanced ArcFace loss in medical image classification. then, based on the Swin-MMC model, we introduce a weak label strategy that effectively mitigates overfitting. Our approach achieves significantly improved results on the test dataset and can be easily used for various datasets and classification tasks. We conduct a series of experiments in the MMAC2023 challenge. In the testing phase, our average performance metric reaches 86.60%. In the further testing phase, our model’s performance improves to 88.23%, ultimately securing the championship in the MMAC2023 challenge. the codes allowing replication of this study have been made publicly available at https://***/LuliDreamAI/MICCAI_TASK1. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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A Hybrid Propagation Network for Interactive Volumetric image Segmentation 1

引用

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

作者： Shi, Luyue Zhang, Xuanye Liu, Yunbi Han, Xiaoguang Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional user hints, e.g., scribbles and clicks. Currently, interactive segmentation methods for 2D medical images are well studied, while seldom works are conducted on 3D medical volumetric data. Given a 3D volumetric image, the user interaction can only be performed on a few slices, thus the key issue is how to propagate the information over the entire volume for spatial-consistent segmentation. In this paper, we propose a novel hybrid propagation network for interactive segmentation of 3D medical images. Our proposed method consists of two key designs, including a slice propagation network (denoted as SPN) for transferring user hints to adjacent slices to guide the segmentation slice-by-slice and a volume propagation network (denoted as VPN) for propagating user hints over the entire volume in a global manner. Specifically, as for SPN, we adopt a memory-augmented network, which utilizes the information of segmented slices (memory slices) to propagate interaction information. To use interaction information propagated by VPN, a feature-enhanced memory module is designed, in which the volume segmentation information from the latent space of VPN is introduced into the memory module of SPN. In such a way, the interactive segmentation can leverage both advantages of volume and slice propagation, thus improving the volume segmentation results. We perform experiments on two commonly-used 3D medical datasets, with the experimental results indicating that our method outperforms the state-of-the-art methods. Our code is available at https://***/luyueshi/Hybrid-Propagation.

关键词： Interactive segmentation Hybrid propagation 3D medical images

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Poisson2Sparse: Self-supervised Poisson Denoising from a Single image 25th

Poisson2Sparse: Self-supervised Poisson Denoising from a Sin...

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

作者： Ta, Calvin-Khang Aich, Abhishek Gupta, Akash Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9783031164521;9783031164514

image enhancement approaches often assume that the noise is signal independent, and approximate the degradation model as zero-mean additive Gaussian. However, this assumption does not hold for biomedical imaging systems where sensor-based sources of noise are proportional to signal strengths, and the noise is better represented as a Poisson process. In this work, we explore a sparsity and dictionary learning-based approach and present a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data. Specifically, we approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network that enforces sparsity with respect to the weights of the network. Since the sparse representations are based on the underlying image, it is able to suppress the spurious components (noise) in the image patches, thereby introducing implicit regularization for denoising tasks through the network structure. Experiments on two bio-imaging datasets demonstrate that our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM. Our qualitative results demonstrate that, in addition to higher performance on standard quantitative metrics, we are able to recover much more subtle details than other compared approaches. Our code is made publicly available at https://***/tacalvin/Poisson2Sparse.

关键词： image enhancement

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Semi-supervised medical image Classification with Temporal Knowledge-Aware Regularization 25th

Semi-supervised Medical Image Classification with Temporal K...

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

作者： Yang, Qiushi Liu, Xinyu Chen, Zhen Ibragimov, Bulat Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ Copenhagen Dept Comp Sci DK-2100 Copenhagen Denmark

ISBN: (纸本)9783031164521;9783031164514

Semi-supervised learning (SSL) for medical image classification has achieved exceptional success on efficiently exploiting knowledge from unlabeled data with limited labeled data. Nevertheless, recent SSL methods suffer from misleading hard-form pseudo labeling, exacerbating the confirmation bias issue due to rough training process. Moreover, the training schemes excessively depend on the quality of generated pseudo labels, which is vulnerable against the inferior ones. In this paper, we propose TEmporal knowledge-Aware Regularization (TEAR) for semisupervised medical image classification. Instead of using hard pseudo labels to train models roughly, we design Adaptive Pseudo Labeling (AdaPL), a mild learning strategy that relaxes hard pseudo labels to soft-form ones and provides a cautious training. AdaPL is built on a novel theoretically derived loss estimator, which approximates the loss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the model to learn discriminative representations in an unsupervised manner. the core principle of IPH is to maintain the harmonization of clustered prototypes across different iterations. Both AdaPL and IPH can be easily incorporated into prior pseudo labeling-based models to extract features from unlabeled medical data for accurate classification. Extensive experiments on three semi-supervised medical image datasets demonstrate that our method outperforms state-of-theart approaches. the code is available at littps://***/City U-AIM-Group/TEAR.

关键词： Semi-supervised learning medical image classification Temporal knowledge Pseudo labeling Prototype harmonizing

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Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework with Cycle-Consistent Constraints 25th

Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework wi...

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

作者： Zhang, Jiadong Cui, Zhiming Jiang, Caiwen Zhang, Jingyang Gao, Fei Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai 200230 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Positron emission tomography (PET) is an important medical imaging technique, especially for brain and cancer disease diagnosis. Modern PET scanner is usually combined with computed tomography (CT), where CT image is used for anatomical localization, PET attenuation correction, and radiotherapy treatment planning Considering radiation dose of CT image as well as increasing spatial resolution of PET image, there is a growing demand to synthesize CT image from PET image (without scanning CT) to reduce risk of radiation exposure. However, most existing works perform learning-based image synthesis to construct cross-modality mapping only in the image domain, without considering of the projection domain, leading to potential physical inconsistency. To address this problem, we propose a novel PET-CT synthesis framework by exploiting dual-domain information (i.e., image domain and projection domain). Specifically, we design both image domain network and projection domain network to jointly learn high-dimensional mapping from PET to CT. the image domain and the projection domain can be connected together with a forward projection (FP) and a filtered back projection (FBP). To further help the PET-to-CT synthesis task, we also design a secondary CT-toPET synthesis task with the same network structure, and combine the two tasks into a bidirectional mapping framework with several closed cycles. More importantly, these cycles can serve as cycle-consistent losses to further help network training for better synthesis performance. Extensive validations on the clinical PET-CT data demonstrate the proposed PET-CT synthesis framework outperforms the state-of-the-art (SOTA) medical image synthesis methods with significant improvements.

关键词： medical image synthesis Dual-domain information Cycle-consistent constraint

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