医学图像

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1. TshFNA-Examiner：甲状腺细胞学图像的核分割和癌症评估框架

柯晶1, 朱俊超2, 杨鑫1, 张浩林3, 孙宇翔1, 王嘉怡1, 鲁亦舟4, 沈逸卿5, 刘晟6, 蒋伏松7, 黄琴8

J Shanghai Jiaotong Univ Sci 2024, 29 (6): 945-957. DOI: 10.1007/s12204-024-2743-y

摘要（269）

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通过甲状腺细针穿刺（FNA）可以评估癌症风险，获得预后信息，并指导后续护理或手术。生物检验数字化和深度学习技术推动了计算病理学的发展。然而，目前仍然缺乏，可以与医生基本水平相匹配的，针对复杂细胞病理学图像的系统性诊断系统。研究中，我们设计了一个深度学习框架，用于定量评估甲状腺细针穿刺图像的癌症风险，该框架名为TshFNA-Examiner。在TshFNA-Examiner中，通过细胞核分割神经网络检测与诊断医学信息强相关的细胞密集区域；通过分类神经网络按照报告甲状腺细胞病理学（TBSRTC）系统对细胞级图像子块进行分类，同时使用半监督网络基于未标记数据对分类网络进行增强。研究了从2019年到2022年收集的333例甲状腺细针穿刺样本，分为I到VI级，并完成了像素级和图像级的图像子块标注。通过综合指标和多个任务评估了TshFNA-Examiner，以证明其优于最先进的深度学习方法。细胞区域分割的平均性能达到了0.931 Dice系数和0.871 Jaccard指数。癌症风险分类器按照TBSRTC标准达到了0.959 Macro-F1-score、0.998 Macro-AUC和0.959准确率。通过利用大量未标记数据进行半监督学习，相应的指标可以提高至0.970 Macro-F1-score、0.999 Macro-AUC和0.970 准确率。在临床实践中，TshFNA-Examiner可以帮助细胞学家以便捷方式可视化深度学习网络的输出，以促进最终决策的制定。

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2. 短波红外时间分辨成像研究进展

徐杨, 李万万

J Shanghai Jiaotong Univ Sci 2024, 29 (1): 29-36. DOI: 10.1007/s12204-022-2547-x

摘要（303）

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相比于传统近红外一区（700~900nm）窗口，短波红外（900~1700nm）能够提供更深的组织穿透深度和更小的光散射干扰，在生物活体成像上具有巨大潜力。基于传统光谱域成像的局限性，时间分辨成像技术利用时间维度特性，能够完全消除生物体自发荧光，提供更高的信噪比和灵敏度。该成像技术不依赖于组织成分和组织厚度的差异，具有实际的体内定量检测价值。由于镧系上转换纳米材料具有长寿命、光化学性质稳定、形貌可控、易于表面改性而且荧光寿命调控手段多样的特点，当前相关的时间分辨成像技术几乎都是围绕镧系上转换纳米材料开展。本文以广泛使用的几种镧系离子发光中心作为切入点，系统地介绍了近些年来的短波红外时间分辨成像技术的研究进展。

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3. 基于对抗学习和迭代优化的视网膜血管分割

顾闻，徐奕

J Shanghai Jiaotong Univ Sci 2024, 29 (1): 73-80. DOI: 10.1007/s12204-022-2479-5

摘要（244）

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由于数据量小、血管细小、图像对比度低等特点，视网膜血管分割是一项具有挑战性的医学任务。为了解决这些问题，文中引入了一种新的卷积神经网络，同时利用了对抗学习和循环神经网络的优势。采用递归单元迭代设计网络，逐步优化输入视网膜图像的分割结果。循环单元保留高级语义信息，用于特征重用，从而输出足够精细的分割图，而不是粗掩码。此外，对抗性损失对分割的血管区域施加了完整性和连通性约束，从而大大减少了分割的拓扑错误。在DRIVE数据集上的实验结果表明，该方法的AUC和灵敏度分别达到98.17%和80.64%。与其他现有的最先进方法相比，该方法在视网膜血管分割方面取得了更好的效果。

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4. 无监督口腔内窥镜图像拼接算法

黄荣，常青，张扬

J Shanghai Jiaotong Univ Sci 2024, 29 (1): 81-90. DOI: 10.1007/s12204-022-2513-7

摘要（377）

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口腔内窥镜图像拼接算法通过配准、拼接等处理获取宽视野口腔图像以满足辅助诊断的需求。与自然图像相比，口腔内窥镜图像的纹理特征少。然而，传统的基于特征的图像拼接方法严重依赖于特征提取的质量，在拼接特征较少的图像时，往往无法令人满意。此外，由于手持拍摄，拍摄的图像之间存在较大的深度和视角差异，这也给图像拼接带来了挑战。为了克服上述问题，提出了一种基于重叠区域提取和深度特征丢失的无监督口腔内窥镜图像拼接算法。在配准阶段，通过绘制多边形交点来提取输入图像的重叠区域进行特征点筛选，并在三层特征金字塔结构上由粗到精进行单应性估计。此外，使用深度特征而不是像素值来计算损失，以强调深度差异在单应性估计中的重要性。最后，对拼接后的图像进行从特征到像素的重构，消除了视差过大带来的伪影。我们的方法在UDIS-D数据集和我们的口腔内窥镜图像数据集上与基于特征和先前基于深度的方法进行了比较。实验结果表明，该算法具有较高的单应性估计精度和较好的视觉质量，可有效应用于口腔内窥镜图像拼接。

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5. 基于约瑟夫遍历和超混沌Lorenz系统的医学图像加密

杨娜，张淑霞，白牡丹，李珊珊

J Shanghai Jiaotong Univ Sci 2024, 29 (1): 91-108. DOI: 10.1007/s12204-022-2555-x

摘要（226）

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本研究提供了一种基于约瑟夫遍历和超混沌Lorenz系统的医学图像加密方案。首先，利用超混沌系统生成超混沌序列，超混沌序列被用于算法的置乱阶段和扩散阶段；其次，在置乱阶段，利用约瑟夫置乱对图像进行初始置乱，然后利用猫映射对图像进行再次置乱。最后，利用生成的超混沌序列和异或操作对图像进行正向扩散和逆向扩散，以此改变图像的像素值，从而进一步隐藏图像中的有效信息。另外，明文图像的信息被用于生成算法中的密钥，这增加了抵抗明文攻击的能力。实验结果和安全性分析表明，该方案能够根据医学图像的特征有效地隐藏明文图像信息，并能够抵抗常见类型的攻击。此外，该方案在鲁棒性实验中表现良好，能够解决远程医疗中的图像信息丢失问题。这对今后的研究具有积极意义。

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6. 基于课程学习训练的聚合注意力网络Multi-SEANet用于MRI图像的格里森级别组无创预测

沈傲1, 2，胡冀苏2, 3，金鹏飞4，周志勇2，钱旭升2, 3，郑毅2，包婕4，王希明4，戴亚康1, 2

J Shanghai Jiaotong Univ Sci 2024, 29 (1): 109-119. DOI: 10.1007/s12204-022-2502-x

摘要（171）

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格里森分级组(Gleason grade group, GG)是评估前列腺癌恶性程度的重要依据，但需要侵入性活检才能获得病理结果。为了无创地预测GG，提出了一种基于课程学习训练的集成注意力模块的多尺度卷积神经网络的自动预测方法。首先，提出了基于感兴趣区域图像的病灶注意力图，并与残存注意力模块相融合，使网络更加关注病灶区域。其次，结合特征金字塔网络，使网络更好地学习病灶区域的多尺度信息。最后，在网络训练中，提出了基于视觉评价与病理分级一致性差异的课程学习网络训练框架，进一步提高了网络的预测性能。实验结果表明，该方法在预测GG性能方面优于传统网络模型。二次加权Kappa结果为0.4711，用于评估临床显著性癌症的阳性预测值为0.9369。

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7. 基于加权异构图谱的增量式疾病自动诊断方法

田圆圆，金衍瑞，李志远，刘金磊，刘成良

J Shanghai Jiaotong Univ Sci 2024, 29 (1): 120-130. DOI: 10.1007/s12204-022-2537-z

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该文目标是构建一个能实现多科室级别的基于症状的疾病自动诊断模型。但构建分类几千种疾病的模型、同时收集成千上万种疾病-症状数据集这两个任务是现有研究难以解决的。基于“知识图谱即是模型”的想法，提出了通过不断学习数据中的经验知识，增量式地注入到知识图谱中，以此来构建一个注入“经验”的知识模型。即通过增量式学习、注入来解决数据收集问题，通过将知识图谱模型化、容器化来解决超多分类问题。首先通过图谱融合构建了一份异构知识图谱并设计了一个实体链接方法。然后对于每份数据集构建一个自适应的神经网络模型，利用数据实现统计学初始化和模型训练。最后将学习完成的神经网络模型中权重和偏置更新到异构图谱中。对于增量过程，同时考虑了数据增量和类别增量两种情况。在三份公共数据集上评估了模型在当前数据集上的诊断效果，以及类别增量后对历史数据集的抗遗忘能力两个性能；与经典模型相比，诊断正确率分别平均提高了5%、2%和15%；同时模型在增量学习下具有较好的抗遗忘能力。

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8. 利用半监督学习提高结肠镜息肉检出率

姚乐宇1，何凡1,3，彭海霞2，王晓峰2，周璐2，黄晓霖1,3

J Shanghai Jiaotong Univ Sci 2023, 28 (4): 441-. DOI: 10.1007/s12204-022-2519-1

摘要（254）

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结直肠癌是人类最大的健康威胁之一，每年夺去数千人的生命。结肠镜检查是临床实践中检查肠壁、在早期发现并切除息肉、防止息肉恶性化和发展成为癌症的金标准。近年来，计算机辅助息肉检测系统被广泛应用于结肠镜检查中以提高结肠镜检查的质量，提高息肉的检出率。目前，最有效的计算机辅助系统是用机器学习方法进行构建的。然而，开发这样的计算机辅助检测系统需要有经验的医生从结肠镜检查视频中标记大量图像数据，这一过程极其耗时、费力且十分昂贵。一种可能的解决方案是采用半监督学习，它可以在不必标注所有数据的数据集上建立检测系统。在本文中，基于最先进的对象检测方法和半监督学习技术，我们设计并实现了一个半监督结肠镜息肉检测系统，该系统的实现包括四个主要步骤：（1）使用所有标记数据进行标准监督训练；（2）对未标记的数据进行推理以获得伪标签；（3）将一系列强数据增强方法应用于未标记数据和伪标记；（4）将标记的数据、未标记的数据与其伪标签组合以重新训练检测器。我们在公开数据集和原始私有数据集上对半监督学习系统进行了评估，并证明了其有效性。此外，半监督学习系统的推理速度也可以满足实时运转的要求。

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9. 基于全局-局部人脸图像的双路径胶囊网络特纳综合征集成诊断方法研究

刘璐

J Shanghai Jiaotong Univ Sci 2023, 28 (4): 459-. DOI: 10.1007/s12204-022-2491-9

摘要（132）

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特纳综合征是一种染色体异常疾病，对女性病人的成长造成极大的危害。及时诊断对该病患者具有重要意义。然而，现有的临床筛查方法相对耗时且费用昂贵。相关研究人员提出使用机器学习方法进行特纳综合征诊断，但是这些方法的诊断准确率有待提升。因此，面向特纳综合征诊断任务，提出一种基于全局-局部人脸图像的双路径胶囊网络集成方法。具体地，对特纳综合征人脸图像进行预处理，并在医疗专家的指导下，将人脸图像分割为8部分具有医学意义的局部人脸图像；然后，基于完整人脸图像和8部分局部图像进行双路径胶囊网络模型训练，以小样本学习方法解决模型训练过程中面临的样本不足问题；最后，以基于概率的集成方法对9个特纳综合征人脸分类模型进行集成。通过对基础分类模型进行分析，发现眼部区域和鼻子区域的异常面容与特纳综合征疾病具有强相关性。实验结果显示，该集成方法对特纳综合征诊断任务具有一定的有效性，能够取得0.9241的最高诊断准确率。

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10. Dlung：无监督少镜头差异呼吸运动建模

陈培芝1,2, 郭逸凡1, 王大寒1,2, 陈金铃1,3,4

J Shanghai Jiaotong Univ Sci 2023, 28 (4): 536-. DOI: 10.1007/s12204-022-2525-3

摘要（234）

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肺部图像配准在肺部分析应用中具有重要作用，比如呼吸运动建模。基于无监督学习的图像配准方法可以在不需要监督的情况下计算变形，因此受到了广泛的关注。但是，需要注意的是，它们有两个缺点：它们不能处理数据不足的问题，也不能保证微分同胚（保留拓扑结构）属性，特别是当肺部扫描中存在较大形变时。本文提出了一种基于无监督少样本学习的微分同胚肺部图像配准方法，称为Dlung。我们采用微调解决数据不足的问题，并采用缩放-平方层来实现微分同胚配准。在实验中，我们进行了空间时间（4D）图像上的配准，并与基准方法进行了全面比较。Dlung取得了最高的微分同胚准确率，它使用有限的数据构建了准确和快速的呼吸运动模型。这项研究扩展了我们对呼吸运动建模的认识。

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11. Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

CHANG Qing∗ (常青), YANG Wenyou (杨文友), CHEN Lanlan (陈兰岚)

J Shanghai Jiaotong Univ Sci 2022, 27 (4): 452-462. DOI: 10.1007/s12204-022-2428-3

摘要（211）

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Medical image registration is widely used in image-guided therapy and image-guided surgery to esti- mate spatial correspondence between planning and treatment images. However, most methods based on intensity have the problems of matching ambiguity and ignoring the influence of weak correspondence areas on the overall registration. In this study, we propose a novel general-purpose registration algorithm based on free-form defor- mation by non-subsampled contourlet transform and saliency map, which can reduce the matching ambiguities and maintain the topological structure of weak correspondence areas. An optimization method based on Markov random fields is used to optimize the registration process. Experiments on four public datasets from brain, car- diac, and lung have demonstrated the general applicability and the accuracy of our algorithm compared with two state-of-the-art methods.

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12. Breast Pathological Image Classification Based on VGG16 Feature Concatenation

LIU Min (刘敏), YI Ming (易鸣), WU Minghu∗ (武明虎), WANG Juan (王娟), HE Yu (何宇)

J Shanghai Jiaotong Univ Sci 2022, 27 (4): 473-484. DOI: 10.1007/s12204-021-2398-x

摘要（291）

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Breast cancer is one of the malignancies that endanger women’s health all over the world. Considering that there is some noise and edge blurring in breast pathological images, it is easier to extract shallow features of noise and redundant information when VGG16 network is used, which is affected by its relative shallow depth and small convolution kernel. To improve the pathological diagnosis of breast cancers, we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network. First, in order to improve the problems of small dataset size and unbalanced data samples, the original BreakHis dataset is processed by data augmentation technologies, such as geometric transformation and color enhancement. Then, to reduce noise and edge blurring in breast pathological images, we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator, which makes the extraction of shallow features by VGG16 model more accurate. Based on transfer learning, the network model trained with the expanded dataset is called VGG16-1, and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2. The features extracted by VGG16-1 and VGG16-2 are concatenated, and then classified by support vector machine. The final experimental results show that the average accuracy is 98.44%, 98.89%, 98.30% and 97.47%, respectively, when the proposed method is tested with the breast pathological images of 40×, 100×, 200× and 400× on BreakHis dataset.

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13. USSL Net: Focusing on Structural Similarity with Light U-Structure for Stroke Lesion Segmentation

JIANG Zhiguo (蒋志国), CHANG Qing∗ (常青)

J Shanghai Jiaotong Univ Sci 2022, 27 (4): 485-497. DOI: 10.1007/s12204-022-2412-y

摘要（199）

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Automatic segmentation of ischemic stroke lesions from computed tomography (CT) images is of great significance for identifying and curing this life-threatening condition. However, in addition to the problem of low image contrast, it is also challenged by the complex changes in the appearance of the stroke area and the difficulty in obtaining image data. Considering that it is difficult to obtain stroke data and labels, a data enhancement algorithm for one-shot medical image segmentation based on data augmentation using learned transformation was proposed to increase the number of data sets for more accurate segmentation. A deep convolutional neural network based algorithm for stroke lesion segmentation, called structural similarity with light U-structure (USSL) Net, was proposed. We embedded a convolution module that combines switchable normalization, multi-scale convolution and dilated convolution in the network for better segmentation performance. Besides, considering the strong structural similarity between multi-modal stroke CT images, the USSL Net uses the correlation maximized structural similarity loss (SSL) function as the loss function to learn the varying shapes of the lesions. The experimental results show that our framework has achieved results in the following aspects. First, the data obtained by adding our data enhancement algorithm is better than the data directly segmented from the multi- modal image. Second, the performance of our network model is better than that of other models for stroke segmentation tasks. Third, the way SSL functioned as a loss function is more helpful to the improvement of segmentation accuracy than the cross-entropy loss function.

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14. SeRN: A Two-Stage Framework of Registration for Semi-Supervised Learning for Medical Images

JIA Dengqiang* (贾灯强), LUO Xinzhe (罗鑫喆), DING Wangbin (丁王斌),HUANG Liqin (黄立勤), ZHUANG Xiahai (庄吓海)

J Shanghai Jiaotong Univ Sci 2022, 27 (2): 176-189. DOI: 10.1007/s12204-021-2383-4

摘要（297）

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Significant breakthroughs in medical image registration have been achieved using deep neural networks (DNNs). However, DNN-based end-to-end registration methods often require large quantities of data or adequate annotations for training. To leverage the intensity information of abundant unlabeled images, unsupervised registration methods commonly employ intensity-based similarity measures to optimize the network parameters.However, finding a sufficiently robust measure can be challenging for specific registration applications. Weakly supervised registration methods use anatomical labels to estimate the deformation between images. High-level structural information in label images is more reliable and practical for estimating the voxel correspondence of anatomic regions of interest between images, whereas label images are extremely difficult to collect. In this paper, we propose a two-stage semi-supervised learning framework for medical image registration, which consists of unsupervised and weakly supervised registration networks. The proposed semi-supervised learning framework is trained with intensity information from available images, label information from a relatively small number of labeled images and pseudo-label information from unlabeled images. Experimental results on two datasets (cardiac and abdominal images) demonstrate the efficacy and efficiency of this method in intra- and inter-modality medical image registrations, as well as its superior performance when a vast amount of unlabeled data and a small set of annotations are available. Our code is publicly available at https://github.com/jdq818/SeRN.

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15. Interactive Liver Segmentation Algorithm Based on Geodesic Distance and V-Net

KANG Jie* (亢洁), DING Jumin (丁菊敏), LEI Tao (雷涛),FENG Shujie (冯树杰), LIU Gang (刘港)

J Shanghai Jiaotong Univ Sci 2022, 27 (2): 190-201. DOI: 10.1007/s12204-021-2379-0

摘要（290）

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Convolutional neural networks (CNNs) are prone to mis-segmenting image data of the liver when the background is complicated, which results in low segmentation accuracy and unsuitable results for clinical use. To address this shortcoming, an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed. The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results. An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation, which significantly improves the efficiency of segmentation. Results from simulation of the liver tumor segmentation challenge (LiTS) dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention, and enable a fast, interactive liver image segmentation that is convenient for doctors.

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16. Survey of EIT Image Reconstruction Algorithms

ZHANG Mingzhu(张明珠), MA Yixin* (马艺馨), HUANG Ningning (黄宁宁), GE Hao (葛浩)

J Shanghai Jiaotong Univ Sci 2022, 27 (2): 211-218. DOI: 10.1007/s12204-021-2333-1

摘要（251）

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With the recent promotion of clinical applications of electrical impedance tomography (EIT) technology,more scholars have begun studying EIT technology. Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field. What’s more, the EIT reconstruction algorithm requires further improvements in robustness, clarity, etc. The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology; thus, more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems. In this paper, we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.

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17. Evaluation Value of Intravoxel Incoherent Motion Diffusion-Weighted Imaging on Early Efficacy of Magnetic Resonance-Guided High-Intensity Focused Ultrasound Ablation for Uterine Adenomyoma

TANG Na (唐纳), GU Jianjun (顾坚骏), YIN Xiaorui (尹肖睿), YU Rongjiang (虞容江),XU Yuantao (徐元涛), LI Xiang (李想), WANG Han* (王悍)

J Shanghai Jiaotong Univ Sci 2022, 27 (2): 226-230. DOI: 10.1007/s12204-022-2405-x

摘要（279）

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To investigate the evaluation value of intravoxel incoherent motion diffusion-weighted imaging (IVIMDWI) on the early efficacy of magnetic resonance-guided high-intensity focused ultrasound (MRgFUS) ablation for uterine adenomyoma. The clinical and magnetic resonance imaging (MRI) data of 36 patients with uterine adenomyoma before and after MRgFUS treatment in our hospital from January 2018 to December 2018 were retrospectively analyzed. All the 36 patients underwent MRI examination one day before operation and immediately after operation using GE Discovery MR750 3.0T MRI, including conventional sequences (T1WI, T2WI,and T2 fat suppression sequences) plain scan, IVIM-DWI sequences with 9 b values, and contrast enhanced-MRI sequences. The IVIM-DWI quantitative parameters (true diffusion coefficient D, perfusion related diffusion coefficient D?, and perfusion fraction f) of double-exponential model were obtained by using GE ADW 4.7 functool,a postprocessor. SPSS 24.0 software was used to analyze the difference in parameter between the ablation and non-ablation areas of uterine adenomyoma. DWI signal in the ablation area of uterine adenomyoma was increased,and manifested as heterogeneous diffuse high signal, with low central signal and high edge signal. Values of D, D? and f in the ablation area of uterine adenomyoma were significantly lower than those in the non-ablation area,and there was statistical difference between the two (P <0.05). The areas under receiver operating characteristic (ROC) curve of D, D? and f values in the ablation area of uterine adenomyoma were 0.854, 0.898 and 0.924,respectively; the optimal thresholds for the diagnosis of ablation area of uterine adenomyoma were 0.81 × 10 ?3 mm2/s, 4.99×10 ?3 mm2/s and 0.24, respectively; the diagnostic sensitivity was 80.6%, 72.2% and 94.4%, respectively; and the specificity was 91.7%, 97.2% and 94.4%, respectively. IVIM-DWI has a certain clinical value in the evaluation on early efficacy of MRgFUS ablation of uterine adenomyosis.

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18. Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images

WANG Zhiming(王志明), DONG Jingjing (董静静), ZHANG Junpeng∗ (张军鹏)

J Shanghai Jiaotong Univ Sci 2022, 27 (1): 70-80. DOI: 10.1007/s12204-021-2392-3

摘要（340）

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Deep learning based analyses of computed tomography (CT) images contribute to automated diagnosis of COVID-19, and ensemble learning may commonly provide a better solution. Here, we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19. Two ensemble strategies are considered: the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation; voting strategy. A database containing 8 347 CT slices of COVID- 19, common pneumonia and normal subjects was used as training and testing sets. Results show that the novel method can reach a high accuracy of 99.37% (recall: 0.998 1; precision: 0.989 3), with an increase of about 7% in comparison to single-component models. And the average test accuracy is 95.62% (recall: 0.958 7; precision: 0.955 9), with a corresponding increase of 5.2%. Compared with several latest deep learning models on the identical test set, our method made an accuracy improvement up to 10.88%. The proposed method may be a promising solution for the diagnosis of COVID-19.

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19. COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples

BU Ran (卜冉), XIANG Wei∗ (向伟), CAO Shitong (曹世同)

J Shanghai Jiaotong Univ Sci 2022, 27 (1): 81-89. DOI: 10.1007/s12204-021-2393-2

摘要（409）

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The COVID-19 medical diagnosis method based on individual’s chest X-ray (CXR) is achieved difficultly in the initial research, owing to difficulties in identifying CXR data of COVID-19 individuals. At the beginning of the study, infected individuals’ CXRs were scarce. The combination of artificial intelligence (AI) and medical diagnosis has been advanced and popular. To solve the difficulties, the interpretability analysis of AI model was used to explore the pathological characteristics of CXR samples infected with COVID-19 and assist in medical diagnosis. The dataset was expanded by data augmentation to avoid overfitting. Transfer learning was used to test different pre-trained models and the unique output layers were designed to complete the model training with few samples. In this study, the output results of four pre-trained models in three different output layers were compared, and the results after data augmentation were compared with the results of the original dataset. The control variable method was used to conduct independent tests of 24 groups. Finally, 99.23% accuracy and 98% recall rate were obtained, and the visual results of CXR interpretability analysis were displayed. The network of COVID-19 interpretable diagnosis algorithm has the characteristics of high generalization and lightweight. It can be quickly applied to other urgent tasks with insufficient experimental data. At the same time, interpretability analysis brings new possibilities for medical diagnosis.

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20. Application of Deep Learning Method on Ischemic Stroke Lesion Segmentation

ZHANG Yue (张月), LIU Shijie (刘世界), LI Chunlai (李春来), WANG Jianyu (王建宇)

J Shanghai Jiaotong Univ Sci 2022, 27 (1): 99-111. DOI: 10.1007/s12204-021-2273-9

摘要（317）

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Although deep learning methods have been widely applied in medical image lesion segmentation, it is still challenging to apply them for segmenting ischemic stroke lesions, which are different from brain tumors in lesion characteristics, segmentation difficulty, algorithm maturity, and segmentation accuracy. Three main stages are used to describe the manifestations of stroke. For acute ischemic stroke, the size of the lesions is similar to that of brain tumors, and the current deep learning methods have been able to achieve a high segmentation accuracy. For sub-acute and chronic ischemic stroke, the segmentation results of mainstream deep learning algorithms are still unsatisfactory as lesions in these stages are small and diffuse. By using three scientific search engines including CNKI, Web of Science and Google Scholar, this paper aims to comprehensively understand the state-of-the-art deep learning algorithms applied to segmenting ischemic stroke lesions. For the first time, this paper discusses the current situation, challenges, and development directions of deep learning algorithms applied to ischemic stroke lesion segmentation in different stages. In the future, a system that can directly identify different stroke stages and automatically select the suitable network architecture for the stroke lesion segmentation needs to be proposed.

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