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

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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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Evaluation and Improvement of Segment Anything Model for Interactive Histopathology image Segmentation 26th

Evaluation and Improvement of Segment Anything Model for Int...

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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： Kim, SeungKyu Oh, Hyun-Jic Min, Seonghui Jeong, Won-Ki Korea Univ Dept Comp Sci & Engn Coll Informat Seoul South Korea

ISBN: (纸本)9783031474002;9783031474019

With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we evaluate SAM's performance in zero-shot and fine-tuned scenarios on histopathology data, with a focus on interactive segmentation. Additionally, we compare SAM with other state-of-the-art interactive models to assess its practical potential and evaluate its generalization capability with domain adaptability. In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability. To improve SAM's limited local refinement ability and to enhance prompt stability while preserving its core strengths, we propose a modification of SAM's decoder. the experimental results suggest that the proposed modification is effective to make SAM useful for interactive histology image segmentation. the code is available at https://***/hvcl/SAM_Interactive_Histopathology

关键词： Segment Anything Model Histopathology image Analysis Interactive Segmentation Foundation Models

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5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2017 Held in Conjunction with 20th international conference on medical image computing and computer-assisted intervention, MICCAI 2017

5th Workshop on Computational Methods and Clinical Applicati...

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5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2017 Held in Conjunction with 20th international conference on medical image computing and computer-assisted intervention, MICCAI 2017

ISBN: (纸本)9783319741123

the proceedings contain 13 papers. the special focus in this conference is on Computational Methods and Clinical Applications in Musculoskeletal Imaging. the topics include: Automatic full femur segmentation from computed tomography datasets using an atlas-based approach;classification of osteoporotic vertebral fractures using shape and appearance modelling;DSMS-FCN: A deeply supervised multi-scale fully convolutional network for automatic segmentation of intervertebral disc in 3D MR images;shape-aware deep convolutional neural network for vertebrae segmentation;Automated characterization of body composition and frailty with clinically acquired CT;unfolded cylindrical projection for rib fracture diagnosis;3D cobb angle measurements from scoliotic mesh models with varying face-vertex density;Automatic localization of the lumbar vertebral landmarks in CT images with context features;Joint multimodal segmentation of clinical CT and MR from hip arthroplasty patients;reconstruction of 3D muscle fiber structure using high resolution cryosectioned volume;Segmentation of pathological spines in CT images using a two-way CNN and a collision-based model.

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ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histopathology image Analysis 3rd

ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histop...

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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: (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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Operating Critical Machine Learning Models in Resource Constrained Regimes 26th

Operating Critical Machine Learning Models in Resource Const...

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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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9th international Workshop on Machine Learning in medical Imaging, MLMI 2018 held in conjunction with the 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

9th International Workshop on Machine Learning in Medical Im...

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9th international Workshop on Machine Learning in medical Imaging, MLMI 2018 held in conjunction with the 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

ISBN: (纸本)9783030009182

the proceedings contain 46 papers. the special focus in this conference is on Machine Learning in medical Imaging. the topics include: Synthesizing dynamic MRI using long-term recurrent convolutional networks;Automatically designing CNN architectures for medical image segmentation;rotation invariance and directional sensitivity: Spherical harmonics versus radiomics features;can dilated convolutions capture ultrasound video dynamics?;topological correction of infant cortical surfaces using anatomically constrained U-net;Self-taught learning with residual sparse autoencoders for HEp-2 cell staining pattern recognition;semantic-aware generative adversarial nets for unsupervised domain adaptation in chest X-ray segmentation;brain status prediction with non-negative projective dictionary learning;classification of pancreatic cystic neoplasms based on multimodality images;robust contextual bandit via the capped- l2 Norm for Mobile Health intervention;retinal blood vessel segmentation using a fully convolutional network – transfer learning from patch- to image-level;combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics;Latent3DU-net: Multi-level latent shape space constrained 3D U-net for automatic segmentation of the proximal femur from radial MRI of the hip;Adversarial image registration with application for MR and TRUS image fusion;Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset;competition vs. concatenation in skip connections of fully convolutional networks;Ensemble of multi-sized FCNs to improve white matter lesion segmentation;automatic accurate infant cerebellar tissue segmentation with densely connected convolutional network;nuclei detection using mixture density networks;attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs.

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Towards Generalised Neural Implicit Representations for image Registration 3rd

Towards Generalised Neural Implicit Representations for Imag...

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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: (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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DAST: Differentiable Architecture Search with Transformer for 3D medical image Segmentation 1

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

作者： Yang, Dong Xu, Ziyue He, Yufan Nath, Vishwesh Li, Wenqi Myronenko, Andriy Hatamizadeh, Ali Zhao, Can Roth, Holger R. Xu, Daguang NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Neural Architecture Search (NAS) has been widely used for medical image segmentation by improving both model performance and computational efficiency. Recently, the Visual Transformer (ViT) model has achieved significant success in computer vision tasks. Leveraging these two innovations, we propose a novel NAS algorithm, DAST, to optimize neural network models with transformers for 3D medical image segmentation. the proposed algorithm is able to search the global structure and local operations of the architecture with a GPU memory consumption constraint. the resulting architectures reveal an effective relationship between convolution and transformer layers in segmentation models. Moreover, we validate the proposed algorithm on large-scale medical image segmentation data sets, showing its superior performance over the baselines. the model achieves state-of-the-art performance in the public challenge of kidney CT segmentation (KiTS'19).

关键词： Neural architecture search Transformer Segmentation

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Robust T-Loss for medical image Segmentation 1

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

作者： Gonzalez-Jimenez, Alvaro Lionetti, Simone Gottfrois, Philippe Groeger, Fabian Pouly, Marc Navarini, Alexander A. Univ Basel Basel Switzerland Lucerne Sch Comp Sci & Informat Technol Rotkreuz Switzerland

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

this paper presents a new robust loss function, the T-Loss, for medical image segmentation. the proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter. this parameter is updated during the backpropagation process, eliminating the need for additional computation or prior information about the level and spread of noisy labels. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets for skin lesion and lung segmentation. We also demonstrate the ability of T-Loss to handle different types of simulated label noise, resembling human error. Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. the project website can be found at https:// robust- ***.

关键词： robust loss medical image segmentation noisy labels

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Boundary Difference over Union Loss for medical image Segmentation 26th

Boundary Difference over Union Loss for Medical Image Segmen...

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

作者： Sun, Fan Luo, Zhiming Li, Shaozi Xiamen Univ Dept Artificial Intelligence Xiamen Fujian Peoples R China

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation. those that do exist often need to be used in combination with other losses and produce ineffective results. To address this issue, we have developed a simple and effective loss called the Boundary Difference over Union Loss (Boundary DoU Loss) to guide boundary region segmentation. It is obtained by calculating the ratio of the difference set of prediction and ground truth to the union of the difference set and the partial intersection set. Our loss only relies on region calculation, making it easy to implement and training stable without needing any additional losses. Additionally, we use the target size to adaptively adjust attention applied to the boundary regions. Experimental results using UNet, TransUNet, and Swin-UNet on two datasets (ACDC and Synapse) demonstrate the effectiveness of our proposed loss function. Code is available at https://***/sunfan-bvb/BoundaryDoULoss.

关键词： medical image segmentation Boundary loss

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