一、近年代表性论文: [1]Han K, Wang S, Chen J, et al. Region Uncertainty Estimation for Medical Image Segmentation with Noisy Labels[J]. IEEE Transactions on Medical Imaging, 2025. (中科院1区, Top期刊, 通讯作者) [2]Han K, Lyu C, Ma L, et al. CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis[C], Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025. (CCF B, 医学计算机领域顶会, 通讯作者) [3]Wan X, Teng Q, Chen J, et al. Eliminating Language Bias for Medical Visual Question Answering with Counterfactual Contrastive Training[C], Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025. (CCF B, 医学计算机领域顶会, 通讯作者) [4]Lyu C, Han K, Liu L, et al. Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2025: 3584236. (中科院1区, Top期刊, 通讯作者) [5]Han K, Sheng V S, Song Y, et al. Deep semi-supervised learning for medical image segmentation: A review[J]. Expert Systems with Applications, 2024: 123052. (中科院1区, Top期刊, 通讯作者) [6]Lyu C, Qiu C, Han K, et al. Automatic medical report generation combining contrastive learning and feature difference[J]. Knowledge-Based Systems, 2024, 305: 112630. (中科院1区, Top期刊, 通讯作者) [7]Liu Z, Teng Q, Song Y, et al. HI-Net: Liver vessel segmentation with hierarchical inter-scale multi-scale feature fusion[J]. Biomedical Signal Processing and Control, 2024, 96: 106604. (中科院2区, IF 4.9) [8]Qiu C, Song Y, Liu Y, et al. MMMViT: Multiscale multimodal vision transformer for brain tumor segmentation with missing modalities[J]. Biomedical Signal Processing and Control, 2024, 90: 105827. (中科院2区, 通讯作者) [9]Chen S, Liu Z, Chen J, et al. Tutor Assisted Feature Distillation[C], ICME, 2024: 1-6. (CCF-B类会议, 通讯作者) [10]Lu Y, Qiu C, Teng Q, et al. LC-SegDiff: Label-Constraint Diffusion Model for Medical Image Segmentation[C], BIBM, 2023: 3305-3312. (CCF-B类会议, 通讯作者) [11]Bu Z, Wang X, Qiu C, et al. Noisy-to-Clean Label Learning for Medical Image Segmentation[C], ICME, 2023: 1553-1558. (CCF-B类会议, 通讯作者) [12]Li H, Rong H, Sheng V S, et al. Cascaded multi-point regression Network for high-quality generic lesion detection[J]. Expert Systems with Applications, 2023, 214: 119141. (中科院1区, Top期刊, 通讯作者) [13]Qiu C, Liu Z, Song Y, et al. RTUNet: Residual transformer UNet specifically for pancreas segmentation[J]. Biomedical Signal Processing and Control, 2023, 79: 104173. (中科院2区, 通讯作者) [14]Hao W, Zhang J, Su J, et al. HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images[J]. Computer Methods and Programs in Biomedicine, 2022, 224: 107003. (中科院2区, Top期刊, 通讯作者) [15]Liu Z, Su J, Wang R, et al. Pancreas Co-segmentation based on dynamic ROI extraction and VGGU-Net[J]. Expert Systems with Applications, 2022, 192: 116444. (中科院1区, Top期刊) [16]Su J, Liu Z, Zhang J, et al. DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function[J]. Knowledge-Based Systems, 2021, 232: 107471. (中科院1区, 通讯作者) [17]Han K, Liu L, Song Y, et al. An effective semi-supervised approach for liver CT image segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(8): 3999-4007. (中科院1区, Top期刊, 通讯作者) [18]Epalle T M, Song Y, Liu Z, et al. Multi-atlas classification of autism spectrum disorder with hinge loss trained deep architectures: ABIDE I results[J]. Applied soft computing, 2021, 107: 107375. (中科院1区, Top期刊, 通讯作者) [19]Liu Z, Xie X, Song Y, et al. MLANet: multi-layer anchor-free network for generic lesion detection[J]. Engineering Applications of Artificial Intelligence, 2021, 102: 104255. (中科院1区, Top期刊) [20]Liu Z, Song Y Q, Sheng V S, et al. Liver CT sequence segmentation based with improved U-Net and graph cut[J]. Expert Systems with Applications, 2019, 126: 54-63. (中科院 1区, Top期刊) [21]Liu Z, Song Y, Sheng V S, et al. MRI and PET image fusion using the nonparametric density model and the theory of variable-weight[J]. Computer Methods and Programs in Biomedicine, 2019, 175: 73-82. (中科院2区, Top期刊) [22]刘哲,胡芮,宋余庆,等.基于对比学习的半监督肝脏血管分割方法[J].华中科技大学学报(自然科学版),2024,52(05):70-75. (梯队期刊, EI) [23]尹静,刘哲,宋余庆,等.基于3D路径聚合高分辨率网络的胰腺分割[J].中国图象图形学报,2023,28(11):3602-3617. (CCF T2, EI) [24]宋余庆,杨东川,刘哲,等.基于DBSE-Net的大田稻穗图像分割[J].农业工程学报,2022,38(13):202-209. (EI) [25]邱成健,刘青山,刘哲,等.基于循环显著性校准网络的胰腺分割方法[J].自动化学报,2022,48(11):2703-2717. (CCF T1, 梯队期刊, EI) [26]毛静怡,宋余庆,刘哲.多尺度深度特征提取的肝脏肿瘤CT图像分类[J].中国图象图形学报,2021,26(07):1704-1715. [27]王瑞豪,刘哲,宋余庆.结合切片上下文信息的多阶段胰腺定位与分割[J].电子学报,2021,49(04):706-715. (CCF T1, EI) [28]宋余庆,谢熹,刘哲,等.基于多层EESP深度学习模型的农作物病虫害识别方法[J].农业机械学报,2020,51(08):196-202. (EI) 刘哲,邹小波,宋余庆,等.基于多特征融合和水平集的碧根果品质检测[J].农业机械学报,2019,50(12):348-356+364. (EI)
二、已授权/申请的发明专利: 1.一钟癌变区域综合检测装詈及方法 (US17/610,158) 基于特征引导网络的图像分类与分割的装置、方法、设备及介质 (US17/612,220) 2.—种肿瘤辅助诊断报告生成方法、装置、电子设备、存储介质 (ZL202110627078.6) 3.一种面向癌症病变检测的特征提取和图文融合方法及系统 (ZL202110705588.0) 4.一种肿瘤影像的病灶区域预测分析方法、系统及终端设备 (ZL202110097268.1) 基于特征引导网络的图像分类与分割的装置、方法、设备及介质(ZL202011597039.8) 5.一种癌变区域综合检测装置及方法 (CN202011327476.8) 6.一种基于视频的行为识别方法(ZL201910831903.7) 7.—种医学图像分割方法 (ZL201610986747.8) 8.—种医学图像聚类方法 (ZL201310209709.8)
三、在研/已结题的项目: 1. 国家自然科学基金面上项目,融合领域知识的多模态数据协同胰腺肿瘤早期诊断研究,2023.01-2026.12 2. 国家自然科学基金面上项目,多组学特征融合的肝癌智能分期分型研究,2020.01-2023.12 3. 国家自然科学基金面上项目,并行架构下基于深度迁移学习的多种模态胰腺肿瘤图像的早期诊断与分析,2018.01-2021.12 4.中国博士后面上项目,基于图像识别的碧根果品质无损检测研究,2017.05-2020.01 5. 国家自然科学基金面上项目,基于多目标优化和栈式稀疏编码的肝脏肿瘤图像识别研究,2016.01-2019.12 6. 国家自然科学基金青年项目,基于非参数密度模型和粗糙集的多模态医学图像处理关键技术研究,2015.01-2017.12 7. 江苏省自然科学基金,粗糙集和非参数正交多项式模型病理学图像处理研究,2013.07-2016.06 8. 江苏省六大人才高峰高层次人才,2019.01-2021.01 9. 江苏高校“青蓝工程”培养对象,2017.06-2020.06
四、近年来的获奖情况: 1. 中国公路学会科技进步一等奖(提名国家奖),排名第一,2018 2. 中国商业联合会中国商业科技进步一等奖,排名第二(校内第一),2017 3. 江苏省科技进步三等奖,排名第三,2019.
五、指导学生获奖情况: 1. 2024年第六届全球人工智能算法精英大赛,国家二等奖1项,国家三等奖2项,江苏省一等奖2项,江苏省三等奖2项 2. 2024年指导研究生获得博士国家奖学金1项,硕士国家奖学金2项 3.2023年睿抗机器人开发者大赛,国家二等奖1项,江苏省赛一等奖1项
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