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Segmentation Distortion: Quantifying Segmentation Uncertainty Under Domain Shift via the Effects of Anomalous Activations 1

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

作者： Lennartz, Jonathan Schultz, Thomas Univ Bonn Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Domain shift occurs when training U-Nets for medical image segmentation with images from one device, but applying them to images from a different device. This often reduces accuracy, and it poses a challenge for uncertainty quantification, when incorrect segmentations are produced with high confidence. Recent work proposed to detect such failure cases via anomalies in feature space: Activation patterns that deviate from those observed during training are taken as an indication that the input is not handled well by the network, and its output should not be trusted. However, such latent space distances primarily detect whether images are from different scanners, not whether they are correctly segmented. Therefore, we propose a novel segmentation distortion measure for uncertainty quantification. It uses an autoencoder to make activations more similar to those that were observed during training, and propagates the result through the remainder of the U-Net. We demonstrate that the extent to which this affects the segmentation correlates much more strongly with segmentation errors than distances in activation space, and that it quantifies uncertainty under domain shift better than entropy in the output of a single U-Net, or an ensemble of U-Nets.

关键词： Uncertainty Quantification image Segmentation Anomaly Propagation

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FedIIC: Towards Robust Federated Learning for Class-Imbalanced medical image Classification 26th

FedIIC: Towards Robust Federated Learning for Class-Imbalanc...

引用

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

作者： Wu, Nannan Yu, Li Yang, Xin Cheng, Kwang-Ting Yan, Zengqiang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases). Existing methods towards this problem mainly focus on training a balanced classifier to eliminate class prior bias among classes, but neglect to explore better representation to facilitate classification performance. In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning. In feature learning, two levels of contrastive learning are designed to extract better class-specific features with imbalanced data in FL. In classifier learning, per-class margins are dynamically set according to real-time difficulty and class priors, which helps the model learn classes equally. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both real-world and simulated multi-source medical imaging data under class imbalance. Code is available at https://***/wnn2000/FedIIC.

关键词： Federated learning Class imbalance Contrastive learning Classification

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Histopathological image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization 26th

Histopathological Image Analysis with Style-Augmented Featur...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Khamankar, Vaibhav Bera, Sutanu Bhattacharya, Saumik Sen, Debashis Biswas, Prabir Kumar Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur India

ISBN: (纸本)9783031474002;9783031474019

Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which may negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to estimate style-mixed versions of image features. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring lower computation. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.

关键词： Domain Shift Domain Generalization Mitotic Figure Style Mixing Feature Domain Augmentation Histopathological image

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Contrastive Masked image-Text Modeling for medical Visual Representation Learning 26th

Contrastive Masked Image-Text Modeling for Medical Visual Re...

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

作者： Chen, Cheng Zhong, Aoxiao Wu, Dufan Luo, Jie Li, Quanzheng Massachusetts Gen Hosp Ctr Adv Med Comp & Anal Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Harvard Univ Sch Engn & Appl Sci Boston MA 02115 USA Massachusetts Gen Brigham Data Sci Off Boston MA USA

ISBN: (纸本)9783031439032;9783031439049

Self-supervised learning (SSL) of visual representations from paired medical images and text reports has recently shown great promise for various downstream tasks. However, previous work has focused on investigating the effectiveness of two major SSL techniques separately, i.e., contrastive learning and masked autoencoding, without exploring their potential synergies. In this paper, we aim to integrate the strengths of these two techniques by proposing a contrastive masked image-text modeling framework for medical visual representation learning. On one hand, our framework conducts cross-modal contrastive learning between masked medical images and text reports, with a representation decoder being incorporated to recover the misaligned information in the masked images. On the other hand, to further leverage masked autoencoding, a masked image is also required to be able to reconstruct the original image itself and the masked information in the text reports. With pre-training on a large-scale medical image and report dataset, our framework shows complementary benefits of integrating the two SSL techniques on four downstream classification datasets. Extensive evaluations demonstrate consistent improvements of our method over state-of-the-art approaches, especially when very scarce labeled data are available. code is available at https://***/cchen-cc/CMITM.

关键词： image-text representation learning masked autoencoding contrastive learning

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Towards AI-Driven Radiology Education: A Self-supervised Segmentation-Based Framework for High-Precision medical image Editing 26th

Towards AI-Driven Radiology Education: A Self-supervised Seg...

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

作者： Kobayashi, Kazuma Gu, Lin Hataya, Ryuichiro Miyake, Mototaka Takamizawa, Yasuyuki Ito, Sono Watanabe, Hirokazu Yoshida, Yukihiro Yoshimura, Hiroki Harada, Tatsuya Hamamoto, Ryuji Natl Canc Ctr Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan Univ Tokyo Tokyo Japan RIKEN Informat R&D & Strategy Headquarters Tokyo Japan Natl Canc Ctr Tokyo Japan Hiroshima Univ Sch Med Hiroshima Japan

ISBN: (纸本)9783031438943;9783031438950

medical education is essential for providing the best patient care in medicine, but creating educational materials using real-world data poses many challenges. For example, the diagnosis and treatment of a disease can be affected by small but significant differences in medical images;however, collecting images to highlight such differences is often costly. Therefore, medical image editing, which allows users to create their intended disease characteristics, can be useful for education. However, existing image-editing methods typically require manually annotated labels, which are labor-intensive and often challenging to represent fine-grained anatomical elements precisely. Herein, we present a novel algorithm for editing anatomical elements using segmentation labels acquired through self-supervised learning. Our self-supervised segmentation achieves pixel-wise clustering under the constraint of invariance to photometric and geometric transformations, which are assumed not to change the clinical interpretation of anatomical elements. The user then edits the segmentation map to produce a medical image with the intended detailed findings. Evaluation by five expert physicians demonstrated that the edited images appeared natural as medical images and that the disease characteristics were accurately reproduced.

关键词： image editing Self-supervised segmentation Education

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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MDViT: Multi-domain Vision Transformer for Small medical image Segmentation Datasets 26th

MDViT: Multi-domain Vision Transformer for Small Medical Ima...

引用

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

作者： Du, Siyi Bayasi, Nourhan Hamarneh, Ghassan Garbi, Rafeef Univ British Columbia Vancouver BC Canada Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031439001;9783031439018

Despite its clinical utility, medical image segmentation (MIS) remains a daunting task due to images' inherent complexity and variability. Vision transformers (ViTs) have recently emerged as a promising solution to improve MIS;however, they require larger training datasets than convolutional neural networks. To overcome this obstacle, data-efficient ViTs were proposed, but they are typically trained using a single source of data, which overlooks the valuable knowledge that could be leveraged from other available datasets. Naivly combining datasets from different domains can result in negative knowledge transfer (NKT), i.e., a decrease in model performance on some domains with non-negligible inter-domain heterogeneity. In this paper, we propose MDViT, the first multi-domain ViT that includes domain adapters to mitigate data-hunger and combat NKT by adaptively exploiting knowledge in multiple small data resources (domains). Further, to enhance representation learning across domains, we integrate a mutual knowledge distillation paradigm that transfers knowledge between a universal network (spanning all the domains) and auxiliary domain-specific network branches. Experiments on 4 skin lesion segmentation datasets show that MDViT outperforms state-of-the-art algorithms, with superior segmentation performance and a fixed model size, at inference time, even as more domains are added. Our code is available at https://***/siyi-wind/MDViT.

关键词： Vision Transformer Data-efficiency Multi-domain Learning medical image Segmentation Dermatology

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Decoupled Consistency for Semi-supervised medical image Segmentation 26th

Decoupled Consistency for Semi-supervised Medical Image Segm...

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

作者： Chen, Faquan Fei, Jingjing Chen, Yaqi Huang, Chenxi Xiamen Univ Schoor Informat Xiamen Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9783031439063;9783031439070

By fully utilizing unlabeled data, the semi-supervised learning (SSL) technique has recently produced promising results in the segmentation of medical images. Pseudo labeling and consistency regularization are two effective strategies for using unlabeled data. Yet, the traditional pseudo labeling method will filter out low-confidence pixels. The advantages of both high- and low-confidence data are not fully exploited by consistency regularization. Therefore, neither of these two methods can make full use of unlabeled data. We proposed a novel decoupled consistency semi-supervised medical image segmentation framework. First, the dynamic threshold is utilized to decouple the prediction data into consistent and inconsistent parts. For the consistent part, we use the method of cross pseudo supervision to optimize it. For the inconsistent part, we further decouple it into unreliable data that is likely to occur close to the decision boundary and guidance data that is more likely to emerge near the high-density area. Unreliable data will be optimized in the direction of guidance data. We refer to this action as directional consistency. Furthermore, in order to fully utilize the data, we incorporate feature maps into the training process and calculate the loss of feature consistency. A significant number of experiments have demonstrated the superiority of our proposed method. The code is available at https:// ***/wxfaaaaa/DCNet.

关键词： Semi-supervised Learning image Segementation Consistency Regularization

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Analysis of Lung Cells with a Novel Segmentation Methodology Using FCN and Deeplab V3 3

Analysis of Lung Cells with a Novel Segmentation Methodology...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Ramya, J. Poongodi, A. Vels Institute Of Science Technology And Advanced Studies Department Of Computer Sciences Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

One of the primary causes of death globally, cancer is estimated to have affected more people in less developed nations than in more developed ones throughout time in terms of both cases and mortality. The unchecked growth of abnormal cells that begins in one or both of the lungs is known as lung cancer. The tumors impede the lungs' capacity to provide oxygen to the circulation as they enlarge. medical images are analyzed using image processing, machine learning, and other technologies to help in early illness identification and treatment. When it comes to diseases like cancer, time is of the essence and should be investigated as soon as feasible. To diagnose lung cancer, we suggested a novel hybrid segmentation technique in this work. Using a fully convolutional network (FCN) and DeeplabV3,we were able to separate lung cancer cells hybridly. According to the findings, our approach performs well and is very accurate when compared to other currently used approaches. The proposed hybrid FCN with the Deeplabv3 model segments the input lung image with foreground and background with 93% and 97% of accuracy. And also shows 86% of IOU and 93% of the score showing the system performs well in the lung cancer segmentation process. © 2024 IEEE.

关键词： Median filters

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M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision 26th

M-GenSeg: Domain Adaptation for Target Modality Tumor Segmen...

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

作者： Alefsen, Malo Vorontsov, Eugene Kadoury, Samuel Ecole Polytech Montreal Montreal PQ Canada CHUM Ctr Rech Montreal PQ Canada Paige Montreal PQ Canada

ISBN: (纸本)9783031439001;9783031439018

Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. This shortcoming, amplified by the limited availability of expert annotated data, has been hampering the deployment of such methods at a larger scale across modalities. To address these issues, we propose M-GenSeg, a new semi-supervised generative training strategy for cross-modality tumor segmentation on unpaired bi-modal datasets. With the addition of known healthy images, an unsupervised objective encourages the model to disentangling tumors from the background, which parallels the segmentation task. Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality. We evaluated the performance on a brain tumor segmentation dataset composed of four different contrast sequences from the public BraTS 2020 challenge data. We report consistent improvement in Dice scores over state-of-the-art domain-adaptive baselines on the unannotated target modality. Unlike the prior art, M-GenSeg also introduces the ability to train with a partially annotated source modality.

关键词： image Segmentation Semi-supervised Learning Unpaired image-to-image Translation

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