As Deep Convolutional Neural Networks (DCNNs) have shown robust performance and results in medical image analysis, a number of deep-learning-based tumor detection methods were developed in recent years. Nowadays, the automatic detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT) is widely applied for the diagnosis and staging of pancreatic cancer. Traditional hand-crafted methods only extract low-level features. Normal convolutional neural networks, however, fail to make full use of effective context information, which causes inferior detection results. In this paper, a novel and efficient pancreatic tumor detection framework aiming at fully exploiting the context information at multiple scales is designed. More specifically, the contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation (DC) Module. A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly. Then, the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions. Finally, the DC Module is specifically designed to capture the interaction information between proposals and surrounding tissues. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately.
A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
German Cancer Research Center (DKFZ) Heidelberg（德国癌症研究中心（DKFZ）海德堡）；Faculty of Mathematics and Computer Science（数学与计算机科学学院）；Heidelberg University（海德堡大学）；Helmholtz Imaging, DKFZ（海德堡影像中心，DKFZ）；Pattern Analysis and Learning Group（模式分析与学习小组）；Department of Radiation Oncology（放射肿瘤科）；Heidelberg University Hospital（海德堡大学医院）；International School of Medicine（国际医学院）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）；Nanjing Drum Tower Hospital（南京鼓楼医院）；Affiliated Hospital of Nanjing University of Chinese Medicine（南京中医药大学附属医院）
Brunel University（布鲁内尔大学）；University College London（伦敦大学学院）
Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consuming and often results in misdiagnosis. In digital pathology, AI-based cancer grading must be extremely accurate in prediction and uncertainty quantification to improve reliability and explainability and are essential for gaining clinicians trust in the technology. We present Bayesian Convolutional Neural Networks for automated pancreatic cancer gra…
A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans
Southwest Jiaotong University（西南交通大学）；Shanghai Jiao Tong University（上海交通大学）；The Second Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第二医院）
PAII Inc.（PAII公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Changhai Hospital（长海医院）；Ping An Technology Co., Ltd.（平安科技（深圳）有限公司）
Accurate and automated tumor segmentation is highly desired since it has the great potential to increase the efficiency and reproducibility of computing more complete tumor measurements and imaging biomarkers, comparing to (often partial) human measurements. This is probably the only viable means to enable the large-scale clinical oncology patient studies that utilize medical imaging. Deep learning approaches have shown robust segmentation performances for certain types of tumors, e.g., brain tumors in MRI imaging, when a training dataset with plenty of pixel-level fully-annotated tumor images…
Early and Prediagnostic Detection of Pancreatic Cancer from Computed Tomography
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Shandong Provincial Qianfoshan Hospital（山东省千佛山医院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Jagiellonian University（杰齐洛尼亚大学）；University of California, San Francisco（加州大学旧金山分校）；University of California, Berkeley（加州大学伯克利分校）；Johns Hopkins Medicine（约翰霍普金斯医学中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Warmia and Mazury（马尔堡大学）；National Medical Institute of the Ministry of Internal Affairs and Administration（内政部和行政管理局国家医疗研究所）；Henan Provincial People’s Hospital & The People’s Hospital of Zhengzhou University（河南省人民医院及郑州大学人民医院）；City of Hope National Medical Center（希望国家医学中心）；Centre Européen de Calcul Atomique et Moléculaire, École Polytechnique Fédérale de Lausanne（欧洲原子和分子计算中心，洛桑联邦理工学院）；Northeastern University（东北大学）；Rutgers University（罗格斯大学）；The First Affiliated Hospital of Soochow University（苏州大学第一附属医院）；Massachusetts General Hospital（麻省总医院）
Symantec Corporation（赛门铁克公司）；University of Central Florida（中佛罗里达大学）；Mayo Clinic（梅奥诊所）
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D Co…
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of rela…
Radboud University Medical Center（拉德堡德大学医学中心）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；National Cancer Institute（美国国家癌症研究所）；The Harker School（哈克学校）；Mayo Clinic（梅奥诊所）
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system usin…
Radboud University Medical Center（拉德堡德大学医学中心）；Radboud Institute for Health Sciences（拉德堡德健康科学研究所）
Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis, however current models still fail to identify small (<2cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients an…
Johns Hopkins University（约翰斯·霍普金斯大学）；University Hospital Basel（巴塞尔大学医院）；Stanford University（斯坦福大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Italian Institute of Technology（意大利技术研究院）；University of Bologna（博洛尼亚大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Johns Hopkins Medicine（约翰斯·霍普金斯医疗集团）
Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset
Johns Hopkins University（约翰斯·霍普金斯大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞总医院）；Jagiellonian University（雅盖隆大学）；Stanford University（斯坦福大学）；Warmian-Masurian Cancer Center（瓦尔米亚-马祖里癌症中心）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Johns Hopkins Medicine（约翰斯·霍普金斯医学）
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）
Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm
Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University（机器与混合智能实验室，放射科，西北大学）；Istanbul Faculty of Medicine, Istanbul University（伊斯坦布尔大学医学学院）；Department of Biomedical Engineering and Radiology, University of Wisconsin-Madison（生物医学工程与放射科，威斯康星大学麦迪逊分校）；Department of Preventive Medicine, Northwestern University（预防医学系，西北大学）；Division of Gastroenterology and Hepatology, New York University（消化内科与肝病科，纽约大学）；Department of Electrical, Electronic and Computer Engineering, University of Catania（电气、电子和计算机工程系，卡塔尼亚大学）；NVIDIA；Department of Radiology, Columbia University（放射科，哥伦比亚大学）；Department of Radiology and Nuclear Medicine, Erasmus Medical Center（放射科与核医学科，埃因霍温医学院）；Department of Gastroenterology and Hepatology, Erasmus Medical Center（消化内科与肝病科，埃因霍温医学院）；Department of Radiology, New York University（放射科，纽约大学）；Division of Gastroenterology and Hepatology, Mayo Clinic Florida（消化内科与肝病科，迈阿密诊所佛罗里达分部）；Department of Gastroenterology and Hepatology, Northwestern University（消化内科与肝病科，西北大学）
Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs). However, in order to obtain good performance, these research achievements rely on hundreds of well-annotated WSIs. In this study, we tackle the tumor localization and detection problem under the setting of few labeled whole slide images and introduce a patch-based analysis pipeline based on the latest reverse knowledge distillation architecture. To address the extremely unbalanced n…
Deep learning classifiers for characterization of whole slide tissue morphology require large volumes of annotated data to learn variations across different tissue and cancer types. As is well known, manual generation of digital pathology training data is time consuming and expensive. In this paper, we propose a semi-automated method for annotating a group of similar instances at once, instead of collecting only per-instance manual annotations. This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which c…
Automatic Endoscopic Ultrasound Station Recognition with Limited Data
Amrita Center for Cybersecurity Systems & Networks, Amrita Vishwa Vidyapeetham（阿姆里塔网络安全系统与网络中心，阿姆里塔大学）；Amrita Institute of Medical Sciences（阿姆里塔医学科学研究所）
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sinai Health System（西奈健康系统）；McMaster University（麦克马斯特大学）；Hamilton Health Sciences（汉密尔顿健康科学）；Juravinski Hospital and Cancer Centre（朱拉文斯基医院及癌症中心）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) have been shown to outperform these feature-based models in computer vision tasks. However, training a CNN from scratch needs a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-b…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）
This paper reports Deep LOGISMOS approach to 3D tumor segmentation by incorporating boundary information derived from deep contextual learning to LOGISMOS - layered optimal graph image segmentation of multiple objects and surfaces. Accurate and reliable tumor segmentation is essential to tumor growth analysis and treatment selection. A fully convolutional network (FCN), UNet, is first trained using three adjacent 2D patches centered at the tumor, providing contextual UNet segmentation and probability map for each 2D patch. The UNet segmentation is then refined by Gaussian Mixture Model (GMM) a…
Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline
UiT The Arctic University of Norway（挪威北极圈大学）；University Hospital of North Norway（挪威北部大学医院）
Eindhoven University of Technology（埃因霍温理工大学）；Catharina Ziekenhuis（凯瑟琳娜医院）；Philips Research（飞利浦研究院）
Pancreatic cancer is one of the global leading causes of cancer-related deaths. Despite the success of Deep Learning in computer-aided diagnosis and detection (CAD) methods, little attention has been paid to the detection of Pancreatic Cancer. We propose a method for detecting pancreatic tumor that utilizes clinically-relevant features in the surrounding anatomical structures, thereby better aiming to exploit the radiologist's knowledge compared to other, conventional deep learning approaches. To this end, we collect a new dataset consisting of 99 cases with pancreatic ductal adenocarcinoma (P…
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outl…
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. To provide radiologists with tumor locations, we adopt a segmentation framework to classify CT volumes by checking if at least a sufficient number of voxels is segmented as tumors. To quantitatively analyze our method, we collect and voxelwisely l…
CECT-Mamba: a Hierarchical Contrast-enhanced-aware Model for Pancreatic Tumor Subtyping from Multi-phase CECT
Southeast University（东南大学）；Medical School of Southeast University（东南大学医学院）；Zhongda Hospital（中大医院）；Zhejiang University（浙江大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Medical College, Fudan University Shanghai Cancer Center（复旦大学上海医学院附属肿瘤医院）；School of Computer Science, University of Illinois（伊利诺伊大学计算机科学学院）
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）
Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from…
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Pharmacy and Biotechnology, University of Bologna（博洛尼亚大学药学与生物技术系）；Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia（意大利技术研究院生物分子纳米技术中心）；Faculty of Mathematics and Computer Science, Jagiellonian University（杰齐兰大学数学与计算机科学学院）；Department of Cardiology and Internal Medicine, University of Warmia and Mazury（马尔堡大学心内科与内科系）；Diagnostic and Treatment Center Gammed（Gammed诊断与治疗中心）；Department of Bioengineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校生物工程系）；Department of General Surgery, Peking University Third Hospital（北京大学第三医院普外科）；Department of Radiology & Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射科与生物医学成像系）；Department of Bioengineering, University of California, Berkeley（加州大学伯克利分校生物工程系）；Department of Radiation Oncology, Johns Hopkins School of Medicine（约翰霍普金斯医学院放射肿瘤学系）；NVIDIA Code, Models & Data（NVIDIA代码、模型与数据）
Kyoto University（京都大学）；Kyoto University Hospital（京都大学医院）
Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were genera…
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Pancreas Centre, First Affiliated Hospital, Nanjing Medical University（南京医科大学第一附属医院胰腺中心）；Department of General Surgery, First Affiliated Hospital, Nanjing Medical University（南京医科大学第一附属医院普外科）；Big Data Computing Center of Southeast University（东南大学大数据计算中心）
Ulm University（乌尔姆大学）；Katharinenhospital（卡塔琳娜医院）；Graz University of Technology（格拉茨技术大学）；BioTechMed（生物技术医学）
Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segm…
TextSAM-EUS: Text Prompt Learning for SAM to Accurately Segment Pancreatic Tumor in Endoscopic Ultrasound
Concordia University（康科迪亚大学）；Jewish General Hospital（犹太总医院）；McGill University Faculty of Medicine（麦吉尔大学医学院）；Lady Davis Institute for Medical Research（拉德克利夫医学研究 institute）
PAII Inc.（PAII 公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Ping An Technology（平安科技）
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. Surgery remains the best chance of a potential cure for patients who are eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients of the same stage and received similar treatments. Accurate preoperative prognosis of resectable PDACs for personalized treatment is thus highly desired. Nevertheless, there are no automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC. Tumor attenuation chang…
3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer
University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；University of Rochester Medical Center（罗切斯特大学医学中心）
Graz University of Technology（格拉茨技术大学）；BioTechMed（生物技术医学）；Shanghai Jiao Tong University（上海交通大学）；Katharinenhospital（卡塔琳娜医院）；Ulm University（乌尔姆大学）
Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases…
Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
LTCI, Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎电信学院LTCI实验室）；Guerbet Research（盖尔贝特研究中心）；Department of Radiology, Hospital of Annecy-Genevois, Université de Paris（巴黎大学阿讷西-热内瓦医院放射科）；Sorbonne Université, CNRS, LIP6（索邦大学、法国国家科学研究中心、LIP6实验室）
Peking Union Medical College Hospital（北京协和医院）；Sun Yat-sen University（中山大学）
The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images. Computer-aided diagnosis (CAD) can potentially address the shortage of pathologists in ROSE. However, the cancerous patterns vary significantly between different samples, making the CAD task extremely challenging. Besides, the ROSE images have complicated perturbations regarding color distribution, brightness, and contrast due to different staining qualities and various acquisition device types. To address these chall…
The Harker School（哈克学校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pionee…
Ping An Technology（平安科技）；Changhai Hospital（长海医院）；PAII Inc.（PAII公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors. Preoperative prediction of LN metastasis from non-invasive CT imaging is highly desired, as it might be straightforwardly used to guide the following neoadjuvant treatment decision and surgical planning. Most studies only capture the tumor characteristics in CT imaging to implicitly infer LN metastasis and very few work exploit direct LN's CT imaging information. To the b…
Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center（病理学系，医学学院，俄亥俄州立大学韦克斯纳医学中心）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，俄亥俄州立大学）
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Provincial People’s Hospital（广东省人民医院）；Shengjing Hospital（盛京医院）
Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院北京生物医学工程高精尖创新中心）；Department of Gastroenterology, Peking Union Medical College Hospital（北京协和医院消化内科）；Department of Pathology, Peking Union Medical College Hospital（北京协和医院病理科）；School of Electronics & Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）
Pancreatic cancer is one of the most malignant cancers in the world, which deteriorates rapidly with very high mortality. The rapid on-site evaluation (ROSE) technique innovates the workflow by immediately analyzing the fast stained cytopathological images with on-site pathologists, which enables faster diagnosis in this time-pressured process. However, the wider expansion of ROSE diagnosis has been hindered by the lack of experienced pathologists. To overcome this problem, we propose a hybrid high-performance deep learning model to enable the automated workflow, thus freeing the occupation of…
Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction
Tongji University（同济大学）；Tongji Hospital（同济医院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Cancer Research UK Cambridge Institute（英国癌症研究剑桥研究所）；University of Cambridge（剑桥大学）
Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark
Sycai Technologies SL, Scientific；Technical Department, Barcelona, Spain；Universit\" a tsklinikum Erlangen, Department of Radiology of the Uniklinikum Erlangen (UKER), Erlangen, Germany；University Hospital Erlangen, Imaging Science Institute, Erlangen, Germany；ICUBE Laboratory, CNRS UMR-7357, University of Strasbourg, Strasbourg, France；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China；University of Chinese Academy of Sciences, Beijing, China；German Cancer Research Center (DKFZ), Medical Image Computing (E230), Heidelberg, Germany；Hospital de Matar\' o , Matar\' o , Spain；Hospital de Sant Pau i la Santa Creu, Diagnostic Imaging Department, Barcelona, Spain；Institut de Recerca Sant Pau - CERCA, Advanced Medical Imaging, Artificial Intelligence；Imaging-Guided Therapy Research Group, Barcelona, Spain；TECNALIA, Basque Research
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
Renji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院）；Tencent AI Lab（腾讯AI实验室）；College of Computer Science, Sichuan University（四川大学计算机学院）
