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Learning Incrementally to Segment Multiple Organs in a CT image 1

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

作者： Liu, Pengbo Wang, Xia Fan, Mengsi Pan, Hongli Yin, Minmin Zhu, Xiaohong Du, Dandan Zhao, Xiaoying Xiao, Li Ding, Lian Wu, Xingwang Zhou, S. Kevin Univ Sci & Technol China Ctr Med Imaging Robot Analyt Comp & Learning MIRA Sch Biomed Engn Suzhou Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Anhui Med Univ Affiliated Hosp 1 Hefei Anhui Peoples R China Huawei Cloud Comp Technol Co Ltd Dongguan Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

there exists a large number of datasets for organ segmentation, which are partially annotated and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to incrementally learn a multi-organ segmentation model. In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. While IL is notorious for its 'catastrophic forgetting' weakness in the context of natural image analysis, we experimentally discover that such a weakness mostly disappears for CT multi-organ segmentation. To further stabilize the model performance across the IL stages, we introduce a light memory module and some loss functions to restrain the representation of different categories in feature space, aggregating feature representation of the same class and separating feature representation of different classes. Extensive experiments on five open-sourced datasets are conducted to illustrate the effectiveness of our method.

关键词： Incremental learning Partially labeled datasets Multi-organ segmentation

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Spatiotemporal Attention for Early Prediction of Hepatocellular Carcinoma Based on Longitudinal Ultrasound images 1

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

作者： Zhang, Yiwen Hu, Chengguang Zhong, Liming Song, Yangda Sun, Jiarun Li, Meng Dai, Lin Zhou, Yuanping Yang, Wei Southern Med Univ Sch Biomed Engn Guangzhou Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc Guangzhou Peoples R China Southern Med Univ Nanfang Hosp Dept Gastroenterol Guangzhou Peoples R China Southern Med Univ Nanfang Hosp Dept Infect Dis & Hepatol Unit Guangzhou Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Early screening is an important way to reduce the mortality of hepatocellular carcinoma (HCC) and improve its prognosis. As a noninvasive, economic, and safe procedure, B-mode ultrasound is currently the most common imaging modality for diagnosing and monitoring HCC. However, because of the difficulty of extracting effective image features and modeling longitudinal data, few studies have focused on early prediction of HCC based on longitudinal ultrasound images. In this paper, to address the above challenges, we propose a spatiotemporal attention network (STA-HCC) that adopts a convolutional-neural-network- transformer framework. the convolutional neural network includes a feature-extraction backbone and a proposed regions-of-interest attention block, which learns to localize regions of interest automatically and extract effective features for HCC prediction. the transformer can capture long-range dependencies and nonlinear dynamics from ultrasound images through a multihead self-attention mechanism. Also, an age-based position embedding is proposed in the transformer to embed a moreappropriate positional relationship among the longitudinal ultrasound images. Experiments conducted on our dataset of 6170 samples collected from 619 cirrhotic subjects show that STA-HCC achieves impressive performance, with an area under the receiver-operating-characteristic curve of 77.5%, an accuracy of 70.5%, a sensitivity of 69.9%, and a specificity of 70.5%. the results show that our method achieves state-of-the-art performance compared with other popular sequence models.

关键词： Hepatocellular carcinoma Early prediction Deep learning Attention Longitudinal data

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Identifying and Combating Bias in Segmentation Networks by Leveraging Multiple Resolutions 25th

Identifying and Combating Bias in Segmentation Networks by L...

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

作者： Henschel, Leonie Kuegler, David Andrews, Derek S. Nordahl, Christine W. Reuter, Martin German Ctr Neurodegenerat Dis DZNE Bonn Germany Univ Calif Davis MIND Inst Davis CA USA Univ Calif Davis Dept Psychiat & Behav Sci Davis CA USA Harvard Med Sch Dept Radiol Boston MA 02115 USA Mass Gen Hosp AA Martinos Ctr Biomed Imag Boston MA 02114 USA

ISBN: (纸本)9783031164439;9783031164422

Exploration of bias has significant impact on the transparency and applicability of deep learning pipelines in medical settings, yet is so far woefully understudied. In this paper, we consider two separate groups for which training data is only available at differing image resolutions. For group H, available images and labels are at the preferred high resolution while for group L only deprecated lower resolution data exist. We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions. Our results demonstrate that single-resolution training settings result in significant loss of volumetric group differences that translate to erroneous segmentations as measured by DSC and subsequent classification failures on the low resolution group. We further explore how training data across resolutions can be used to combat this systematic bias. Specifically, we investigate the effect of image resampling, scale augmentation and resolution independence and demonstrate that biases can effectively be reduced with multi-resolution approaches.

关键词： image resolution

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LifeLonger: A Benchmark for Continual Disease Classification 25th

LifeLonger: A Benchmark for Continual Disease Classification

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

作者： Derakhshani, Mohammad Mahdi Najdenkoska, Ivona Van Sonsbeek, Tom Zhen, Xiantong Mahapatra, Dwarikanath Worring, Marcel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Inception Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031164347;9783031164330

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. the encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. the code repository, data partitions and baseline results for the complete benchmark are publicly available(1) (https://***/mmderalchshani/LifeLonger).

关键词： medical continual learning Disease classification medical image analysis

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AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic medical image Matching 25th

AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Ma...

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

作者： Nguyen, Khanh Nguyen, Huy Hoang Tiulpin, Aleksei Univ Oulu Oulu Finland

ISBN: (纸本)9783031164521;9783031164514

this paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). the main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss - an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method - a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. the codes allowing replication of this study have been made publicly available at https://***/Oulu-IMEDS/AdaTriplet.

关键词： Deep learning Content-based image retrieval Forensic matching

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SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological image Analysis 25th

SETMIL: Spatial Encoding Transformer-Based Multiple Instance...

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

作者： Zhao, Yu Lin, Zhenyu Sun, Kai Zhang, Yidan Huang, Junzhou Wang, Liansheng Yao, Jianhua Xiamen Univ Sch Informat Dept Comp Sci Xiamen 361005 Peoples R China Al Lab Tencent Shenzhen 518000 Peoples R China Cent South Univ Sch Basic Med Sci Changsha 410013 Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Considering the huge size of the gigapixel whole slide image (WSI), multiple instance learning (MIL) is normally employed to address pathological image analysis tasks, where learning an informative and effective representation of each WSI plays a central role but remains challenging due to the weakly supervised nature of MIL. To this end, we present a novel Spatial Encoding Transformer-based MIL method, SETMIL, which has the following advantages. (1) It is a typical embedded-space MIL method and therefore has the advantage of generating the bag embedding by comprehensively encoding all instances with a fully trainable transformer-based aggregating module. (2) SETMIL leverages spatial-encoding-transformer layers to update the representation of an instance by aggregating both neighbouring instances and globally-correlated instances simultaneously. (3) the joint absolute-relative position encoding design in the aggregating module further improves the context-information-encoding ability of SETMIL. (4) SETMIL designs a transformer-based pyramid multi-scale fusion module to comprehensively encode the information with different granularity using multi-scale receptive fields and make the obtained representation enriched with multi-scale context information. Extensive experiments demonstrated the superior performance of SETMIL in challenging pathological image analysis tasks such as gene mutation and lymph node metastasis prediction.

关键词： Multiple instance learning Pathological image analysis Transformer Position encoding

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Bag of Tricks for Ultra-widefield Fundus image Quality Assessment 1st

Bag of Tricks for Ultra-widefield Fundus Image Quality Ass...

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1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Sun, Junfeng Wang, Xinliang Gu, Yunchao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9783031893872

Ultra-widefield fundus images provide a broad view and play an important role in the integration of deep learning and healthcare. therefore, it is important to obtain high-quality ultra-widefield fundus image data. We participated in Task 1 of the Ultra-Widefield Fundus Imaging for Diabetic Retinopathy Challenge, focusing on ultra-widefield fundus image quality assessment. the performance of the image quality assessment can be improved by tricks in the training and inference procedure, such as data augmentation, label smoothing, image resizing, and integration of deep learning models. We employ the bag of tricks to enhance the performance of ultra-widefield fundus image quality assessment. In this paper, we examine a series of such tricks and empirically assess their impact on the final model through experiments. the experiments demonstrate that by combining these improvements, significant improvements in prediction performance can be achieved. We achieve a test score of 0.9644 in the image quality assessment task of the challenge. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damage detection

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Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological image Classification 25th

Spatial-Hierarchical Graph Neural Network with Dynamic Struc...

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

作者： Hou, Wentai Huang, Helong Peng, Qiong Yu, Rongshan Yu, Lequan Wang, Liansheng Xiamen Univ Sch Informat Informat & Commun Engn Dept Xiamen Peoples R China Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Pok Fu Lam Hong Kong Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Graph neural network (GNN) has achieved tremendous success in histological image classification, as it can explicitly model the notion and interaction of different biological entities (e.g., cell, tissue and etc.). However, the potential of GNN has not been fully unleashed for histological image analysis due to (1) the fixed design mode of graph structure and (2) the insufficient interactions between multi-level entities. In this paper, we proposed a novel spatial-hierarchical GNN framework (SHGNN) equipped with a dynamic structure learning (DSL) module for effective histological image classification. Compared with traditional GNNs, the proposed framework has two compelling characteristics. First, the DSL module integrates the positional attribute and semantic representation of entities to learn the adjacency relationship of them during the training process. Second, the proposed SHGNN can extract rich and discriminative features by mining the spatial features of different entities via graph convolutions and aggregating the semantic of multi-level entities via a vision transformer (ViT) based interaction mechanism. We evaluate the proposed framework on our collected colorectal cancer staging (CRCS) dataset and the public breast carcinoma subtyping (BRAGS) dataset. Experimental results demonstrate that our proposed method yield superior classification results compared to state-of-the-arts.

关键词： Histological image classification Graph neural network Dynamic structure Hierarchical representation Vision transformer

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ConTrans: Improving Transformer with Convolutional Attention for medical image Segmentation 25th

ConTrans: Improving Transformer with Convolutional Attention...

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

作者： Lin, Ailiang Xu, Jiayu Li, Jinxing Lu, Guangming Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9783031164439;9783031164422

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant architectures in medical image segmentation. Although CNNs can efficiently capture local representations, they experience difficulty establishing long-distance dependencies. Comparably, ViTs achieve impressive success owing to their powerful global contexts modeling capabilities, but they may not generalize well on insufficient datasets due to the lack of inductive biases inherent to CNNs. To inherit the merits of these two different design paradigms while avoiding their respective limitations, we propose a concurrent structure termed ConTrans, which can couple detailed localization information with global contexts to the maximum extent. ConTrans consists of two parallel encoders, i.e., a Swin Transformer encoder and a CNN encoder. Specifically, the CNN encoder is progressively stacked by the novel Depthwise Attention Block (DAB), with the aim to provide the precise local features we need. Furthermore, a well-designed Spatial-Reduction-Cross-Attention (SRCA) module is embedded in the decoder to form a comprehensive fusion of these two distinct feature representations and eliminate the semantic divergence between them. this allows to obtain accurate semantic information and ensure the up-sampling features with semantic consistency in a hierarchical manner. Extensive experiments across four typical tasks show that ConTrans significantly outperforms state-of-the-art methods on ten famous benchmarks.

关键词： medical image segmentation Transformer Convolution Neural Network Cross-attention

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Classification of Myopic Maculopathy images with Self-supervised Driven Multiple Instance Learning Network 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： Li, Jiawen Soon, Jaehyeon Zhang, Qilai Zhang, Qifan He, Yonghong Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopia is a high-incidence disease that widely exists across various regions. If left unaddressed, it may escalate into high myopia. the leading cause of visual impairment is myopic maculopathy. Currently, certain deep-learning techniques have been employed for the analysis of images depicting myopic maculopathy in fundus photography. these methods are dedicated to assisting physicians in efficient disease diagnosis. In our work, a deep learning framework is introduced to classify images of five different severities of myopic maculopathy. First, we employ a diffusion model to generate a series of images for data augmentation to alleviate the pressure of uneven distribution of categories in training datasets, then we divide images into multiple patches and perform self-supervised learning to generate patch-level feature embeddings. Building upon the above foundation, an aggregator is proposed based on multiple instance learning to achieve image-level classification. We demonstrate the effectiveness of this method in four sufficient experiments with three key evaluation metrics of quadratic-weighted kappa, F1 score, and specificity. Our approach secured the tenth position in the Myopic Maculopathy Analysis Challenge 2023 (MICCAI MMAC 2023). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Supervised learning

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