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TshFNA-Examiner: A Nuclei Segmentation and Cancer Assessment Framework for Thyroid Cytology Image

KE Jing¹(柯晶), ZHU Junchao² (朱俊超), YANG Xin¹(杨鑫), ZHANG Haolin³ (张浩林), SUN Yuxiang¹(孙宇翔), WANG Jiayi¹(王嘉怡), LU Yizhou⁴(鲁亦舟), SHEN Yiqing⁵(沈逸卿), LIU Sheng⁶(刘晟), JIANG Fusong⁷(蒋伏松), HUANG Qin⁸(黄琴)

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

Abstract （234）

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Examining thyroid fine-needle aspiration (FNA) can grade cancer risks, derive prognostic information, and guide follow-up care or surgery. The digitization of biopsy and deep learning techniques has recently enabled computational pathology. However, there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images, which can match physician-level basic perception. In this study, we design a deep learning framework, thyroid segmentation and hierarchy fine-needle aspiration (TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA image. In the TshFNA-Examiner, cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network; cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) system, by a classification neural network which is further enhanced by leveraging unlabeled data. A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied, with pixel-wise and image-wise image patches annotated. Empirically, TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning approaches. The average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of 0.871. The cancer risk classifier achieves a macro-F1-score of 0.959, macro-AUC of 0.998, and accuracy of 0.959 following TBSRTC. The corresponding metrics can be enhanced to a macro-F1-score of 0.970, macro-AUC of 0.999, and accuracy of 0.970 by leveraging informative unlabeled data. In clinical practice, TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision.

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Time-Resolved Imaging in Short-Wave Infrared Region

XU Yang (徐杨), LI Wanwan∗ (李万万)

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

Abstract （270）

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Compared with the conventional first near-infrared (NIR-I, 700—900 nm) window, the short-wave infrared region (SWIR, 900—1 700 nm) possesses the merits of the increasing tissue penetration depths and the suppression of scattering background, leading to great potential for in vivo imaging. Based on the limitations of the common spectral domain, and the superiority of the time-dimension, time-resolved imaging eliminates the auto-fluorescence in the biological tissue, thus supporting higher signal-to-noise ratio and sensitivities. The imaging technique is not affected by the difference in tissue composition or thickness and has the practical value of quantitative in vivo detection. Almost all the relevant time-resolved imaging was carried out around lanthanide-doped upconversion nanomaterials, owing to the advantages of ultralong luminescence lifetime, excellent photostability, controllable morphology, easy surface modification and various strategies of regulating lifetime. Therefore, this review presents the research progress of SWIR time-resolved imaging technology based on nanomaterials doped with lanthanide ions as luminescence centers in recent years.

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Retinal Vessel Segmentation via Adversarial Learning and Iterative Refinement

GU Wen (顾闻), XU Yi∗ (徐奕)

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

Abstract （208）

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Retinal vessel segmentation is a challenging medical task owing to small size of dataset, micro blood vessels and low image contrast. To address these issues, we introduce a novel convolutional neural network in this paper, which takes the advantage of both adversarial learning and recurrent neural network. An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually. Recurrent unit preserves high-level semantic information for feature reuse, so as to output a sufficiently refined segmentation map instead of a coarse mask. Moreover, an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions, thus greatly reducing topology errors of segmentation. The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17% and 80.64%, respectively. Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.

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Unsupervised Oral Endoscope Image Stitching Algorithm

HUANG Rong (黄荣), CHANG Qing^∗ (常青), ZHANG Yang （张扬）

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

Abstract （347）

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Oral endoscope image stitching algorithm is studied to obtain wide-field oral images through registration and stitching, which is of great significance for auxiliary diagnosis. Compared with natural images, oral images have lower textures and fewer features. However, traditional feature-based image stitching methods rely heavily on feature extraction quality, often showing an unsatisfactory performance when stitching images with few features. Moreover, due to the hand-held shooting, there are large depth and perspective disparities between the captured images, which also pose a challenge to image stitching. To overcome the above problems, we propose an unsupervised oral endoscope image stitching algorithm based on the extraction of overlapping regions and the loss of deep features. In the registration stage, we extract the overlapping region of the input images by sketching polygon intersection for feature points screening and estimate homography from coarse to fine on a three-layer feature pyramid structure. Moreover, we calculate loss using deep features instead of pixel values to emphasize the importance of depth disparities in homography estimation. Finally, we reconstruct the stitched images from feature to pixel, which can eliminate artifacts caused by large parallax. Our method is compared with both feature-based and previous deep-based methods on the UDIS-D dataset and our oral endoscopy image dataset. The experimental results show that our algorithm can achieve higher homography estimation accuracy, and better visual quality, and can be effectively applied to oral endoscope image stitching.

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Medical Image Encryption Based on Josephus Traversing and Hyperchaotic Lorenz System

YANG Na (杨娜), ZHANG Shuxia (张淑霞), BAI Mudan (白牡丹), LI Shanshan^∗ (李珊珊)

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

Abstract （211）

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This study proposes a new medical image encryption scheme based on Josephus traversing and hyperchaotic Lorenz system. First, a chaotic sequence is generated through hyperchaotic system. This hyperchaotic sequence is used in the scrambling and diffusion stages of the algorithm. Second, in the scrambling process, the image is initially confused by Josephus scrambling, and then the image is further confused by Arnold map. Finally, generated hyperchaos sequence and exclusive OR operation is used for the image to carry on the positive and reverse diffusion to change the pixel value of the image and further hide the effective information of the image. In addition, the information of the plaintext image is used to generate keys used in the algorithm, which increases the ability of resisting plaintext attack. Experimental results and security analysis show that the scheme can effectively hide plaintext image information according to the characteristics of medical images, and is resistant to common types of attacks. In addition, this scheme performs well in the experiments of robustness, which shows that the scheme can solve the problem of image damage in telemedicine. It has a positive significance for the future research.

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Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI

SHEN Ao^1,2‡ (沈傲), HU Jisu^2,3‡ (胡冀苏), JIN Pengfei⁴ (金鹏飞), ZHOU Zhiyong² (周志勇), QIAN Xusheng^2,3 (钱旭升), ZHENG Yi² (郑毅), BAO Jie⁴ (包婕), WANG Ximing^4∗ (王希明), DAI Yakang^1,2∗ (戴亚康)

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

Abstract （147）

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The Gleason grade group (GG) is an important basis for assessing the malignancy of prostate cancer, but it requires invasive biopsy to obtain pathology. To noninvasively evaluate GG, an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning. First, a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area. Second, the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area. Finally, in the network training, a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed, which further improves the prediction performance of the network. Experimental results show that the proposed method is better than the traditional network model in predicting GG performance. The quadratic weighted Kappa is 0.471 1 and the positive predictive value for predicting clinically significant cancer is 0.936 9.

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Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method

TIAN Yuanyuan (田圆圆), JIN Yanrui (金衍瑞), LI Zhiyuan (李志远), LIU Jinlei (刘金磊), LIU Chengliang^∗ (刘成良)

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

Abstract （184）

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The objective of this study is to construct a multi-department symptom-based automatic diagnosis model. However, it is difficult to establish a model to classify plenty of diseases and collect thousands of diseasesymptom datasets simultaneously. Inspired by the thought of “knowledge graph is model”, this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data, and incrementally injecting it into the knowledge graph. Therefore, incremental learning and injection are used to solve the data collection problem, and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems. First, an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion. Then, an adaptive neural network model is constructed for each dataset, and the data is used for statistical initialization and model training. Finally, the weights and biases of the learned neural network model are updated to the knowledge graph. It is worth noting that for the incremental process, we consider both the data and class increments. We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets. Compared with the classical model, the proposed model improves the diagnostic accuracy of the three datasets by 5%, 2%, and 15% on average, respectively. Meanwhile, the model under incremental learning has a better ability to resist forgetting.

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Improving Colonoscopy Polyp Detection Rate Using Semi-Supervised Learning

YAO Leyul (姚乐宇)，HE Fan1,3 (何凡), PENG Haixia2* (彭海霞), WANG Xiaofeng2 (王晓峰)，ZHOU Lu2(周璐), HUANG Xiaolin1,3* (黄晓霖)

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

Abstract （234）

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Colorectal cancer is one of the biggest health threats to humans and takes thousands of lives every year.Colonoscopy is the gold standard in clinical practice to inspect the intestinal wall, detect polyps and remove polypsin early stages, preventing polyps from becoming malignant and forming colorectal cancer instances. In recentyears, computer-aided polyp detection systems have been widely used in colonoscopies to improve the qualityof colonoscopy examination and increase the polyp detection rate. Currently, the most efficient computer-aidedsystems are built with machine learning methods. However, developing such a computer-aided detection systemrequires experienced doctors to label a large number of image data from colonoscopy videos, which is extremelytime-consuming, laborious and expensive. One possible solution is to adopt a semi-supervised learning, which canbuild a detection system on a dataset where part of its data is not necessary to be labeled. In this paper, on thebasis of state-of-the-art object detection method and semi-supervised learning technique, we design and implementa semi-supervised colonoscopy polyp detection system containing four main steps: running standard supervisedtraining with all labeled data; running inference on unlabeled data to obtain pseudo labels; applying a set ofstrong augmentation to both unlabeled data and pseudo label; combining labeled data, and unlabeled data withits pseudo labels to retrain the detector. The semi-supervised learning system is evaluated both on public datasetand our original private dataset and proves its effectiveness. Also, the inference speed of the semi-supervisedlearning system can meet the requirement of real-time operation.

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Ensemble of Two-Path Capsule Networks for Diagnosis of Turner Syndrome Using Global-Local Facial Images

LIU Lu (刘璐)

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

Abstract （127）

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Turner syndrome (TS) is a chromosomal disorder disease that only affects the growth of female patients. Prompt diagnosis is of high significance for the patients. However, clinical screening methods are time-consuming and cost-expensive. Some researchers used machine learning-based methods to detect TS, the performance of which needed to be improved. Therefore, we propose an ensemble method of two-path capsule networks (CapsNets) for detecting TS based on global-local facial images. Specifically, the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians; then, nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images, in which the few-shot learning is utilized to solve the problem of limited data; finally, a probability-based ensemble method is exploited to combine nine classifiers for the classification of TS. By studying base classifiers, we find two meaningful facial areas are more related to TS patients, i.e., the parts of eyes and nose. The results demonstrate that the proposed model is effective for the TS classification task, which achieves the highest accuracy of 0.924 1.

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Dlung: Unsupervised Few-Shot Diffeomorphic Respiratory Motion Modeling

CHEN Peizhi1,2* (陈培芝), GUO Yifan1 (郭逸凡)，WANG Dahan1,2 (王大寒), CHEN Chinling1,3,4* (陈金铃)

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

Abstract （227）

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Lung image registration plays an important role in lung analysis applications, such as respiratory motion modeling. Unsupervised learning-based image registration methods that can compute the deformation without the requirement of supervision attract much attention. However, it is noteworthy that they have two drawbacks: they do not handle the problem of limited data and do not guarantee diffeomorphic (topologypreserving) properties, especially when large deformation exists in lung scans. In this paper, we present an unsupervised few-shot learning-based diffeomorphic lung image registration, namely Dlung. We employ fine-tuning techniques to solve the problem of limited data and apply the scaling and squaring method to accomplish the diffeomorphic registration. Furthermore, atlas-based registration on spatio-temporal (4D) images is performed and thoroughly compared with baseline methods. Dlung achieves the highest accuracy with diffeomorphic properties. It constructs accurate and fast respiratory motion models with limited data. This research extends our knowledge of respiratory motion modeling.

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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

Abstract （205）

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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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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

Abstract （276）

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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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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

Abstract （190）

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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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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

Abstract （293）

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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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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

Abstract （288）

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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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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

Abstract （236）

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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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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

Abstract （271）

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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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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

Abstract （329）

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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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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

Abstract （398）

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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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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

Abstract （302）

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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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