The segmentation of diseases is a popular topic explored by researchers in the field of machine learning. Brain tumors are extremely dangerous and require the utmost precision to segment for a successful surgery. Patients with tumors usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to radiologists to segment and analyze for possible future surgery. To create a second segmentation, it would be beneficial to both radiologists and patients in being more confident in their conclusions. We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation. Using Mask R-CNN, its ResNet backbone being pre-trained on the RSNA pneumonia detection challenge dataset, we can train a model on the Brats2020 Brain Tumor dataset. Center for Biomedical Image Computing & Analytics provides MRI data on patients with and without brain tumors and the corresponding segmentations. We can see how well the method of image subtraction works by comparing it to models without image subtraction through DICE coefficient (F1 score), recall, and precision on the untouched test set. Our model performed with a DICE coefficient of 0.75 in comparison to 0.69 without image subtraction. To further emphasize the usefulness of image subtraction, we compare our final model to current state-of-the-art models to segment tumors from MRI scans.
Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；The Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院暨研究所）；Nanjing University（南京大学）
RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医科大学附属连云港医院）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
University of Minnesota（明尼苏达大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Heidelberg（海德堡大学）；PingAn Technology Co., Ltd（平安科技有限公司）；Shanghai United Imaging Intelligence Inc.（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）；Peking University First Hospital（北京大学第一医院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Background Subtraction with Real-time Semantic Segmentation
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Fraunhofer IGD（弗劳恩霍夫计算机图形学研究所）；TU Darmstadt（达姆施塔特工业大学）
Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluation
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China（生物医学工程学院，深圳大学医学院，深圳大学，深圳，中国）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China（智能医学影像、学习与工程（SMILE）实验室，深圳大学，深圳，中国）；Nuclear Medicine Department, the Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China（核医学科，中山大学第七附属医院，中山大学，深圳，中国）；Department of Radiology, the Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China（放射科，中山大学第七附属医院，中山大学，深圳，中国）；Shenzhen University of Advanced Technology, Shenzhen, China（深圳大学先进技术大学，深圳，中国）
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Universitätsklinikum Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲分子神经科学网络综合癌症中心）
Unleashing Diffusion and State Space Models for Medical Image Segmentation
Department of Biostatistics, School of Global Public Health, New York University（生物统计学系、全球公共卫生学院、纽约大学）；School of Statistics, KLATASDS-MOE, East China Normal University（统计学系、KLATASDS-MOE、华东师范大学）；School of Biomedical Engineering, Southern Medical University（生物医学工程学院、南方医科大学）；Faculty of Biomedical Engineering, Shenzhen University of Advanced Technology（生物医学工程学院、深圳先进技术大学）
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision
James B. Conant High School（詹姆斯·B·柯南特高中）
今天，美国有超过70万人患有脑瘤。除非采取必要的预防措施，否则脑肿瘤可以迅速扩散到大脑和脊髓的其他部位。因此，这种疾病的存活率对于男性和女性都低于40%。对一些人来说，脑肿瘤的结论性和早期诊断可能是生与死的区别。然而，脑肿瘤检测和分割是繁琐且耗时的过程，因为它只能由放射科医生和临床专家完成。使用计算机视觉技术，如Mask R卷积神经网络（Mask R CNN）来检测和分割脑肿瘤，可以减轻人为错误的可能性，同时提高预测准确率。该项目的目标是创建一个用于脑肿瘤检测和分割的辅助诊断工具。迁移学习与Mask R CNN一起使用，并相应地改变必要的参数作为起点。该模型用20个epoch进行训练，然后进行测试。预测分割与地面实况匹配90%。这表明该模型能够在高水平上运行。模型完成后，就创建了在Flask上运行的应用程序。该应用程序将作为医疗专业人员的工具。它允许医生上传患者脑肿瘤MRI图像，以便立即获得每个患者的诊断和分割结果。
John Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利技术研究院）；University of California, San Francisco（旧金山大学）；University of California, Berkeley（伯克利大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利理工学院）；University of California, San Francisco（加州大学旧金山分校）；Istanbul Medipol University（伊斯坦布尔Medipol大学）；University of Zurich（苏黎世大学）；ETH AI Center（ETH人工智能中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Freie Universität Berlin（柏林自由大学）；University of Bern（伯尔尼大学）；IDEXX Laboratories（艾迪克斯实验室）
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Oslo University Hospital（奥斯陆大学医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto Universitario de Tecnologías de la Información y Comunicaciones（信息技术与通信大学学院）
Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China（电子与电气工程系，南方科技大学，深圳，中国）；Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China（电气与电子工程系，香港大学，香港特别行政区，中国）；Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China（放射科，中大医院，医学院，东南大学，南京，中国）；Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China（嘉兴研究所，南方科技大学，嘉兴，中国）
Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translation
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Golisano College of Computing and Information Sciences, Rochester Institute of Technology（罗切斯特理工大学戈利萨诺计算与信息科学学院）
Anatomy-Informed Deep Learning and Radiomics for Automated Neurofibroma Segmentation in Whole-Body MRI
Institute for Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；Center for Biomedical Artificial Intelligence (bAIome)（生物医学人工智能中心）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine（诊断与介入放射学及核医学部门）；Department of Neurology（神经内科部门）
Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI
School of Health Science and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院）；Shanghai General Hospital（上海交通大学附属第一人民医院）
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI
A. K. Choudhury School of Information Technology, University of Calcutta（加尔各答大学A.K.乔杜里信息技术学院）；Rammohan College, University of Calcutta（加尔各答大学拉莫汉学院）；Indian Statistical Institute（印度统计研究院）
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；Sichuan Cancer Hospital（四川省肿瘤医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Universidad Industrial de Santander（桑坦德工业大学）；University of Toulouse（图卢兹大学）；Université Paul Sabatier Toulouse 3（图卢兹第三大学（保罗·萨巴蒂埃大学））；Bicêtre University Hospital（比塞特尔大学医院）；Assistance publique des hôpitaux de Paris（巴黎公共援助医院）；Université Claude Bernard Lyon 1（里昂第一大学（克洛德·贝尔纳里昂第一大学））
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The University of Hong Kong（香港大学）；The First Affiliated Hospital, Zhejiang University（浙江大学第一附属医院）；Hupan Lab（华盼实验室）
