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Transformer-Based Annotation Bias-Aware medical image Segmentation 26th

Transformer-Based Annotation Bias-Aware Medical Image Segmen...

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

作者： Liao, Zehui Hu, Shishuai Xie, Yutong Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aerospace Ground Ocean B Xian 710072 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (纸本)9783031439001;9783031439018

Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixelwise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectively model the varying degrees of preference at the full resolution level. Additionally, the independent pixel-wise Gaussian distribution disregards pixel correlations, leading to a discontinuous boundary. this paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors. TAB employs the Transformer with learnable queries to extract the different preference-focused features. this enables TAB to produce segmentation with various preferences simultaneously using a single segmentation head. Moreover, TAB takes the multivariant normal distribution assumption that models pixel correlations, and learns the annotation distribution to disentangle the stochastic error. We evaluated our TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results suggest that TAB outperforms existing medical image segmentation models which take into account the annotator-related bias. the code is available at https://***/Merrical/TAB.

关键词： medical image segmentation Multiple annotators Transformer Multivariate normal distribution

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Self-adaptive Adversarial Training for Robust medical Segmentation 1

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

作者： Wang, Fu Fu, Zeyu Zhang, Yanghao Ruan, Wenjie Univ Exeter Exeter EX4 4QF Devon England Univ Liverpool Liverpool L69 3BX Merseyside England

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Adversarial training has been demonstrated to be one of the most effective approaches to training deep neural networks that are robust to malicious perturbations. Research on effectively applying it to produce robust 3D medical image segmentation models is ongoing. While few empirical studies have been done in this area, developing effective adversarial training methods for complex segmentation models and high-volume 3D examples is challenging and requires theoretical support. In this paper, we consider the robustness of 3D segmentation tasks from a PAC-Bayes generalisation perceptive and show that reducing the trained models' Lipschitz constant benefits the models' robustness performance. Demonstrating by empirical investigation, we show that adjusting the adversarial iteration can help to reduce the model's Lipschitz constant, enabling a self-adaptive adversarial training strategy. Empirical studies on the medical segmentation decathlon dataset have been done to demonstrate the efficiency of the proposed adversarial training method. Our implementation is available at https://***/TrustAI/SEAT.

关键词： medical image Segmentation Adversarial Training

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Text-Guided Foundation Model Adaptation for Pathological image Classification 26th

Text-Guided Foundation Model Adaptation for Pathological Ima...

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

作者： Zhang, Yunkun Gao, Jin Zhou, Mu Wang, Xiaosong Qiao, Yu Zhang, Shaoting Wang, Dequan Shanghai Jiao Tong Univ Shanghai Peoples R China Rutgers State Univ Newark NJ USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439032;9783031439049

the recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with strong generalizability. Yet pathological image datasets often lack biomedical text annotation and enrichment. Guiding data-efficient image diagnosis from the use of biomedical text knowledge becomes a substantial interest. In this paper, we propose to Connect image and Text Embeddings (CITE) to enhance pathological image classification. CITE injects text insights gained from language models pre-trained with a broad range of biomedical texts, leading to adapt foundation models towards pathological image understanding. through extensive experiments on the PatchGastric stomach tumor pathological image dataset, we demonstrate that CITE achieves leading performance compared with various baselines especially when training data is scarce. CITE offers insights into leveraging in-domain text knowledge to reinforce data-efficient pathological image classification. Code is available at https://***/Yunkun-Zhang/CITE.

关键词： Foundation models Multi-modality Model Adaptation Pathological image classification

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Multi-scale Prototypical Transformer forWhole Slide image Classification 26th

Multi-scale Prototypical Transformer forWhole Slide Image Cl...

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

作者： Ding, Saisai Wang, Jun Li, Juncheng Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the complicated pre-processing to fuse multi-scale information ofWSI. To this end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI classification, which includes a prototypical Transformer (PT) module and a multi-scale feature fusion module (MFFM). the PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture. It substitutes all instances with cluster prototypes, which are then re-calibrated through the self-attention mechanism of Transformer. thereafter, an MFFM is proposed to fuse the clustered prototypes of different scales, which employs MLP-Mixer to enhance the information communication between prototypes. the experimental results on two public WSI datasets demonstrate that the pro-posed MSPT outperforms all the compared algorithms, suggesting its potential applications.

关键词： Whole slide image Multiple instance learning Multi-scale feature Prototypical Transformer

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NASDM: Nuclei-Aware Semantic Histopathology image Generation Using Diffusion Models 26th

NASDM: Nuclei-Aware Semantic Histopathology Image Generation...

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

作者： Shrivastava, Aman Fletcher, P. thomas Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9783031439865;9783031439872

In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings. Nuclei segmentation, for instance, is an important task for diagnosing different cancers. However, training deep learning models for nuclei segmentation requires large amounts of annotated data, which is expensive to collect and label. this necessitates explorations into generative modeling of histopathological images. In this work, we use recent advances in conditional diffusion modeling to formulate a first-of-its-kind nuclei-aware semantic tissue generation framework (NASDM) which can synthesize realistic tissue samples given a semantic instance mask of up to six different nuclei types, enabling pixel-perfect nuclei localization in generated samples. these synthetic images are useful in applications in pathology pedagogy, validation of models, and supplementation of existing nuclei segmentation datasets. We demonstrate that NASDM is able to synthesize high-quality histopathology images of the colon with superior quality and semantic controllability over existing generative methods. Implementation: https://***/4m4n5/NASDM.

关键词： Generative Modeling Histopathology Diffusion Models

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UPCoL: Uncertainty-Informed Prototype Consistency Learning for Semi-supervised medical image Segmentation 26th

UPCoL: Uncertainty-Informed Prototype Consistency Learning f...

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

作者： Lu, Wenjing Lei, Jiahao Qiu, Peng Sheng, Rui Zhou, Jinhua Lu, Xinwu Yang, Yang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Vasc Surg Shanghai Peoples Hosp 9 Shanghai Peoples R China Anhui Med Univ Chaohu Clin Medcial Coll Hefei Peoples R China Anhui Med Univ Sch Biomed Engn Hefei Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples Hosp 9 Shanghai Key Lab Tissue Engn Sch Med Shanghai Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Semi-supervised learning (SSL) has emerged as a promising approach for medical image segmentation, while its capacity has still been limited by the difficulty in quantifying the reliability of unlabeled data and the lack of effective strategies for exploiting unlabeled regions with ambiguous predictions. To address these issues, we propose an Uncertainty-informed Prototype Consistency Learning (UPCoL) framework, which learns fused prototype representations from labeled and unlabeled data judiciously by incorporating an entropy-based uncertainty mask. the consistency constraint enforced on prototypes leads to a more discriminative and compact prototype representation for each class, thus optimizing the distribution of hidden embeddings. We experiment with two benchmark datasets of two-class semi-supervised segmentation, left atrium and pancreas, as well as a three-class multi-center dataset of type B aortic dissection. For all three datasets, UPCoL outperforms the state-of-the-art SSL methods, demonstrating the efficacy of the uncertainty-informed prototype learning strategy (Code is available at https://***/VivienLu/UPCoL).

关键词： Semi-supervised learning Uncertainty assessment Prototype learning medical image segmentation

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Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation 3rd

Importance of Aligning Training Strategy with Evaluation for...

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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： Fu, Yunguan Li, Yiwen Saeed, Shaheer U. Clarkson, Matthew J. Hu, Yipeng UCL London England InstaDeep London England Univ Oxford Oxford England

ISBN: (纸本)9783031537660;9783031537677

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation. In this work, we studied the DDPM-based segmentation model for 3D multiclass segmentation on two large multiclass data sets (prostate MR and abdominal CT). We observed that the difference between training and test methods led to inferior performance for existing DDPM methods. To mitigate the inconsistency, we proposed a recycling method which generated corrupted masks based on the model's prediction at a previous time step instead of using ground truth. the proposed method achieved statistically significantly improved performance compared to existing DDPMs, independent of a number of other techniques for reducing train-test discrepancy, including performing mask prediction, using Dice loss, and reducing the number of diffusion time steps during training. the performance of diffusion models was also competitive and visually similar to non-diffusion-based U-net, within the same compute budget. the JAX-based diffusion framework has been released at https:// ***/mathpluscode/ImgX-DiffSeg.

关键词： image Segmentation Diffusion Model Prostate MR Abdominal CT

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X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior 26th

X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Dee...

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

作者： Cafaro, Alexandre Spinat, Quentin Leroy, Amaury Maury, Pauline Munoz, Alexandre Beldjoudi, Guillaume Robert, Charlotte Deutsch, Eric Gregoire, Vincent Lepetit, Vincent Paragios, Nikos TheraPanacea Paris France Paris Saclay Univ Gustave Roussy Inserm 1030 Villejuif France Ctr Leon Berard Dept Radiat Oncol Lyon France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Paris France

ISBN: (纸本)9783031439988;9783031439995

We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required projections, the patient dose, and the acquisition time. To address this ill-posed problem, we introduce prior knowledge of anatomic structures by training a generative model on 3D CTs of head and neck. We optimize the latent vectors of the generative model to recover a volume that both integrates this prior knowledge and ensures consistency between the reconstructed image and input projections. Our method outperforms recent methods in terms of reconstruction error while being faster and less radiating than current clinical workflow. We evaluate our method in a clinical configuration for radiotherapy.

关键词： image reconstruction Inverse problem Sparse sampling Deep generative model CT

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Differentiable Beamforming for Ultrasound Autofocusing 26th

Differentiable Beamforming for Ultrasound Autofocusing

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

作者： Simson, Walter Zhuang, Louise Sanabria, Sergio J. Antil, Neha Dahl, Jeremy J. Hyun, Dongwoon Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9783031439988;9783031439995

Ultrasound images are distorted by phase aberration arising from local sound speed variations in the tissue, which lead to inaccurate time delays in beamforming and loss of image focus. Whereas state-of-the-art correction approaches rely on simplified physical models (e.g. phase screens), we propose a novel physics-based framework called differentiable beamforming that can be used to rapidly solve a wide range of imaging problems. We demonstrate the generalizability of differentiable beamforming by optimizing the spatial sound speed distribution in a heterogeneous imaging domain to achieve ultrasound autofocusing using a variety of physical constraints based on phase shift minimization, speckle brightness, and coherence maximization. the proposed method corrects for the effects of phase aberration in both simulation and in-vivo cases by improving image focus while simultaneously providing quantitative speed-of-sound distributions for tissue diagnostics, with accuracy improvements with respect to previously published baselines. Finally, we provide a broader discussion of applications of differentiable beamforming in other ultrasound domains.

关键词： Ultrasound image reconstruction Optimization

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Interpretable medical image Classification Using Prototype Learning and Privileged Information 26th

Interpretable Medical Image Classification Using Prototype L...

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

作者： Gallee, Luisa Beer, Meinrad Goetz, Michael Univ Hosp Ulm Expt Radiol Ulm Germany Univ Hosp Ulm Dept Diagnost & Intervent Radiol Ulm Germany Univ Hosp Ulm i2SouI Innovat Imaging Surg Oncol Ulm Ulm Germany

ISBN: (纸本)9783031438943;9783031438950

Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In this work, we investigate whether additional information available during the training process can be used to create an understandable and powerful model. We propose an innovative solution called Proto-Caps that leverages the benefits of capsule networks, prototype learning and the use of privileged information. Evaluating the proposed solution on the LIDC-IDRI dataset shows that it combines increased interpretability with above state-of-the-art prediction performance. Compared to the explainable baseline model, our method achieves more than 6% higher accuracy in predicting both malignancy (93.0%) and mean characteristic features of lung nodules. Simultaneously, the model provides case-based reasoning with prototype representations that allow visual validation of radiologist-defined attributes.

关键词： Explainable AI Capsule Network Prototype Learning

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