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the Role of Subgroup Separability in Group-Fair medical image Classification 1

引用

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

作者： Jones, Charles Roschewitz, Melanie Glocker, Ben Imperial Coll London Dept Comp London England

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

We investigate performance disparities in deep classifiers. We find that the ability of classifiers to separate individuals into subgroups varies substantially across medical imaging modalities and protected characteristics;crucially, we show that this property is predictive of algorithmic bias. through theoretical analysis and extensive empirical evaluation (Code is available at https://***/biomedia-mira/subgroup-separability), we find a relationship between subgroup separability, subgroup disparities, and performance degradation when models are trained on data with systematic bias such as underdiagnosis. Our findings shed new light on the question of how models become biased, providing important insights for the development of fair medical imaging AI.

关键词： medical imaging

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Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for medical image Classification 9th

Probing the Efficacy of Federated Parameter-Efficient Fine-T...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Alkhunaizi, Naif Almalik, Faris Al-Refai, Rouqaiah Naseer, Muzammal Nandakumar, Karthik Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of data silos, and stringent privacy constraints exacerbate this fine-tuning problem in the medical imaging domain, creating a strong need for algorithms that enable collaborative fine-tuning of pre-trained models. Moreover, the large size of these models necessitates the use of parameter-efficient fine-tuning (PEFT) to reduce the communication burden in federated learning. In this work, we systematically investigate various federated PEFT strategies for adapting a Vision Transformer (ViT) model (pre-trained on a large natural image dataset) for medical image classification. Apart from evaluating known PEFT techniques, we introduce new federated variants of PEFT algorithms such as visual prompt tuning (VPT), low-rank decomposition of visual prompts, stochastic block attention fine-tuning, and hybrid PEFT methods like low-rank adaptation (LoRA)+VPT. Moreover, we perform a thorough empirical analysis to identify the optimal PEFT method for the federated setting and understand the impact of data distribution on federated PEFT, especially for out-of-domain (OOD) and non-IID data. the key insight of this study is that while most federated PEFT methods work well for in-domain transfer, there is a substantial accuracy vs. efficiency trade-off when dealing with OOD and non-IID scenarios, which is commonly the case in medical imaging. Specifically, every order of magnitude reduction in fine-tuned/exchanged parameters can lead to a 4% drop in accuracy. thus, the choice of the initial model is critical for the effectiveness of federated PEFT - rather than starting with general vision models, it is preferable to use medical foundation models (if available) learned using in-domain medical image data. Code: https://***/Naiftt/PEFT.

关键词： Vision Transformers Parameter-Efficient Fine-tuning Out-of-Domain Transfer Federated Learning

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A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation 9th

A Federated Learning-Friendly Approach for Parameter-Efficie...

引用

27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Asokan, Mothilal Benjamin, Joseph Geo Yaqub, Mohammad Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data used for pre-training and medical (target) data. However, collecting task-specific medical data for such finetuning at a central location raises many privacy concerns. Although Federated learning (FL) provides an effective means for training on private decentralized data, communication costs in federating large foundation models can quickly become a significant bottleneck, impacting the solution's scalability. In this work, we address this problem of 'efficient communication while ensuring effective learning in FL' by combining the strengths of Parameter-Efficient Fine-tuning (PEFT) with FL. Specifically, we study plug-and-play Low-Rank Adapters (LoRA) in a federated manner to adapt the Segment Anything Model (SAM) for 3D medical image segmentation. Unlike prior works that utilize LoRA and finetune the entire decoder, we critically analyze the contribution of each granular component of SAM on finetuning performance. thus, we identify specific layers to be federated that are very efficient in terms of communication cost while producing on-par accuracy. Our experiments show that retaining the parameters of the SAM model (including most of the decoder) in their original state during adaptation is beneficial because fine-tuning on small datasets tends to distort the inherent capabilities of the underlying foundation model. On Fed-KiTS, our approach decreases communication cost (similar to 48x.) compared to full fine-tuning while increasing performance (similar to 6%. Dice score) in 3D segmentation tasks. Our approach performs similar to SAMed while achieving similar to 2.8x reduction in communication and parameters to be finetuned. We further validate our approach with experiments on Fed-IXI and Prostate MRI datasets. Our code is available at https://***/BioMedIA-MBZUAI/FLAP-SAM.

关键词： Federated Learning Foundation Model 3D medical image Segmentation Parameter-Efficient Fine-Tuning

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ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histopathology image Analysis 1

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Xu, Xuan Kapse, Saarthak Gupta, Rajarsi Prasanna, Prateek SUNY Stony Brook New York NY 11794 USA

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial Networks (GANs) have provided innovative approaches for histopathological image analysis, they suffer from limitations such as mode collapse and overfitting in discriminator. Recently, Denoising Diffusion models have demonstrated promising results in computer vision. these models exhibit superior stability during training, better distribution coverage, and produce high-quality diverse images. Additionally, they display a high degree of resilience to noise and perturbations, making them well-suited for use in digital pathology, where images commonly contain artifacts and exhibit significant variations in staining. In this paper, we present a novel approach, namely ViT-DAE, which integrates vision transformers (ViT) and diffusion autoencoders for high-quality histopathology image synthesis. this marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images. We demonstrate the effectiveness of ViT-DAE on three publicly available datasets. Our approach outperforms recent GAN-based and vanilla DAE methods in generating realistic images.

关键词： Keywords: Histopathology Diffusion Autoencoders Vision Transformers

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Diffusion Transformer U-Net for medical image Segmentation 26th

Diffusion Transformer U-Net for Medical Image Segmentation

引用

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

作者： Chowdary, G. Jignesh Yin, Zhaozheng SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9783031439001;9783031439018

Diffusion model has shown its power on various generation tasks. When applying the diffusion model in medical image segmentation, there are a few roadblocks to remove: the semantic features required for the conditioning of the diffusion process are not well aligned with the noise embedding;and the U-Net backbone employed in these diffusion models is not sensitive to contextual information that is essential during the reverse diffusion process for accurate pixel-level segmentation. To overcome these limitations, we present a cross-attention module to enhance the conditioning from source images, and a transformer based U-Net with multi-sized windows for the extraction of various scales of contextual information. Evaluated on five benchmark datasets with different imaging modalities including Kvasir-Seg, CVC Clinic DB, ISIC 2017, ISIC 2018, and Refuge, our diffusion transformer U-Net achieves great generalization ability and outperforms all the state-of-the-art models on these datasets.

关键词： Diffusion model Transformer U-Net medical image Segmentation

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Operating Critical Machine Learning Models in Resource Constrained Regimes 26th

Operating Critical Machine Learning Models in Resource Const...

引用

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

作者： Selvan, Raghavendra Schon, Julian Dam, Erik B. Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Neurosci Copenhagen Denmark

ISBN: (纸本)9783031474248;9783031474255

the accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. the resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. these large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.

关键词： Resource efficiency image Classification Deep Learning

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Federated Impression for Learning with Distributed Heterogeneous Data 9th

Federated Impression for Learning with Distributed Heterogen...

引用

27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Arya, Atrin Ayromlou, Sana Saadat, Armin Abolmaesumi, Purang Li, Xiaoxiao Univ British Columbia Elect & Comp Engn Dept Vancouver BC V6T IZ4 Canada Vector Inst Toronto ON M5G 0C6 Canada

ISBN: (纸本)9783031776090;9783031776106

Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data, which can help mitigate privacy and data ownership issues. In FL, sub-optimal convergence caused by data heterogeneity is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. through experimentation in this study, we show that data heterogeneity leads to the phenomenon of catastrophic forgetting during local training. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression. To achieve this, we distill the global model resulting from each communication round. Subsequently, we use the synthetic data alongside the local data to enhance the generalization of local training. Extensive experiments show that the proposed method achieves state-of-the-art performance on both the BloodMNIST and Retina datasets, which contain label imbalance and domain shift, with an improvement in classification accuracy of up to 20%. the code is available at https://***/Atrin78/FedImpress.

关键词： Federated Learning Catastrophic Forgetting Data Synthesis Data Heterogeneity

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Annotator Consensus Prediction for medical image Segmentation with Diffusion Models 26th

Annotator Consensus Prediction for Medical Image Segmentatio...

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

作者： Amit, Tomer Shichrur, Shmuel Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9783031439001;9783031439018

A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporate information from multiple annotations and fuse it into a unified segmentation map that reflects the consensus of multiple experts. We evaluate the performance of our method on several datasets of medical segmentation annotated by multiple experts and compare it with the state-of-the-art methods. Our results demonstrate the effectiveness and robustness of the proposed method. Our code is publicly available at https://***/tomeramit/Annotator-Consensus-Prediction.

关键词： Multi annotator image segmentation Diffusion Model

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Memory Replay for Continual medical image Segmentation through Atypical Sample Selection 26th

Memory Replay for Continual Medical Image Segmentation Throu...

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

作者： Bera, Sutanu Ummadi, Vinay Sen, Debashis Mandal, Subhamoy Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is critical for accurate diagnosis, treatment planning and disease monitoring. Existing deep learning-based segmentation models can suffer from catastrophic forgetting, especially when faced with varying patient populations and imaging protocols. Continual learning (CL) addresses this challenge by enabling the model to learn continuously from a stream of incoming data without the need to retrain from scratch. In this work, we propose a continual learning-based approach for medical image segmentation using a novel memory replay-based learning scheme. the approach uses a simple and effective algorithm for image selection to create the memory bank by ranking and selecting images based on their contribution to the learning process. We evaluate our proposed algorithm on three different problems and compare it with several baselines, showing significant improvements in performance. Our study highlights the potential of continual learning-based algorithms for medical image segmentation and underscores the importance of efficient sample selection in creating memory banks.

关键词： Memory Replay Continual Learning medical image Segmentation

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Towards Generalised Neural Implicit Representations for image Registration 1

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作者： Zimmer, Veronika A. Hammernik, Kerstin Sideri-Lampretsa, Vasiliki Huang, Wenqi Reithmeir, Anna Rueckert, Daniel Schnabel, Julia A. Tech Univ Munich Sch Computat Informat & Technol Munich Germany Helmholtz Munich Munich Germany Tech Univ Munich Sch Med Klinikum Rechts Isar Munich Germany Munich Ctr Machine Learning MCML Munich Germany Imperial Coll London Dept Comp London England Kings Coll London London England

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Neural implicit representations (NIRs) enable to generate and parametrize the transformation for image registration in a continuous way. By design, these representations are image-pair-specific, meaning that for each signal a new multi-layer perceptron has to be trained. In this work, we investigate for the first time the potential of existent NIR generalisation methods for image registration and propose novel methods for the registration of a group of image pairs using NIRs. To exploit the generalisation potential of NIRs, we encode the fixed and moving image volumes to latent representations, which are then used to condition or modulate the NIR. Using ablation studies on a 3D benchmark dataset, we show that our methods are able to generalise to a set of image pairs with a performance comparable to pairwise registration using NIRs when trained on N = 10 and N = 120 datasets. Our results demonstrate the potential of generalised NIRs for 3D deformable image registration.

关键词： image registration Neural implicit representation Generalisation Periodic activation functions

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