Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
University of Bedfordshire（贝德福特大学）；Perm State University（彼尔姆国立大学）；Schaeffler Group（舍弗勒集团）；Universiti Brunei Darussalam（文莱达鲁萨兰大学）；Abramov Software GmbH & Co. KG（阿布拉莫夫软件两合有限公司）
Amit Kumar Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, Samuel Abramov
备注：
Accepted in CVPR 2019 Workshop Towards Causal, Explainable and Universal Medical Visual Diagnosis
Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. We find that our proposed model trained with a semi-supervised learning approach by using pseudo labels on PCam-level significantly leads to better performances to strong CNN baseline on the AUC metric.
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…
University of Texas Health Science Center-Houston, Medical School（德克萨斯大学健康科学中心休斯顿分校医学院）
Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for each case. Our deep learning study focuses on breast cancer screening with only a small set of image patches from any sentinel lymph node, positive or negative for metastasis, to detect changes in tumor environment and not in the tumor itself. We design a convolutional neural network in the Python language to build a diagnostic model for this purpose. The excellent results fro…
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）
Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer
Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of Chicago（芝加哥大学）；University of California, Berkeley（加州大学伯克利分校）；Public Health Agency of Catalonia（加泰罗尼亚公共卫生局）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；University of Pennsylvania（宾夕法尼亚大学）
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology Inc（宜远智能医疗科技有限公司）；Xiamen University（厦门大学）；The Hong Kong Polytechnic University（香港理工大学）
淋巴结转移是乳腺癌最重要的诊断指标之一，传统上由病理学家在显微镜下观察。近年来，计算机组织学诊断已成为医学图像计算中发展最快的领域之一，它减轻了病理学家的工作量，降低了误诊率。然而，由于具有巨大分辨率的大规模数据和硬模拟的存在，从整个载玻片图像中自动检测淋巴结转移仍然是一个具有挑战性的问题。在本文中，我们提出了一种新的框架，利用全卷积网络进行有效的推理，以满足临床实践的速度要求，同时在不同的偏移下重建密集的预测，以确保对微观和宏观转移的准确检测。采用异步样本预取和硬否定挖掘策略，可以有效地训练网络。在2016年Camelyon Grand Challenge的基准数据集上进行的大量实验证实了我们方法的有效性。与最先进的方法相比，我们的方法在肿瘤定位任务上以更快的速度取得了优异的性能，并且在WSI分类任务上超过了人类的性能。
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（健康科技与信息学系，香港理工大学）；Centre for Smart Health and School of Nursing, The Hong Kong Polytechnic University（智能健康中心及护理学院，香港理工大学）；Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong（影像与介入放射科系，中国香港大学）
Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Proscia Inc.（Proscia公司）
淋巴结转移是乳腺癌最重要的预后指标之一。然而，目前人工检查前哨淋巴结的诊断程序非常耗时且主观。病理学家必须手动扫描整个数字全载玻片图像（WSI），以寻找转移区域，这些区域有时只能在高分辨率下检测到，或者完全隐藏在人类视觉皮层之外。2015年10月至2016年4月，国际生物医学成像研讨会（ISBI）举办了Camelyon Grand Challenge 2016，以众包淋巴结转移自动检测的想法和算法。使用可推广的染色归一化技术和Proscia Pathology云计算平台，我们在数百万个组织和肿瘤图像瓦片上训练了一个深度卷积神经网络，以对我们的全载玻片图像测试集进行基于载玻片的评估，灵敏度为0.96，特异性为0.89，AUC评分为0.90。我们的结果表明，我们的平台可以自动扫描任何WSI的转移区域，而无需对相应的染色谱进行机构校准。
The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset
University of California San Francisco（加州大学旧金山分校）；University of California, San Diego（加州大学圣地亚哥分校）；University of Pennsylvania（宾夕法尼亚大学）；Duke University（杜克大学）
Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging. In this paper we investigate how to leverage existing high-quality datasets most efficiently in multi-task settings for closely related tasks. Specifically, we will explore different training and domain adaptation…
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）；Technical University of Munich（慕尼黑工业大学）；Stony Brook University（石溪大学）
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distribution of tumor infiltrating lymphocytes within whole slide images (WSIs). A deep learning classification model was trained using 50*50 square micron patches extracted from the WSIs. We simulated a FL environment in which a dataset, generated from WSIs of cancer from numerous anatomical sites available by The Cancer Genome Atlas repository, is partitioned in 8 different…
UiT The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）
Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate and time consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in whole slide images (WSIs) of standard diagnostic haematoxylin and eosin stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification…
Shenzhen University（深圳大学）；The Ohio State University（俄亥俄州立大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In recent years, advances in the development of whole-slide images have laid a foundation for the utilization of digital images in pathology. With the assistance of computer images analysis that automatically identifies tissue or cell types, they have greatly improved the histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. Unlike traditional image cropping methods that are only suitable for large resolution images, we propose a novel data augmentation metho…
Universitätsklinikum Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心（CCC ER-EMN））
Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting t…
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Radboud University Medical Center（拉德堡德大学医学中心）；Technical University of Munich（慕尼黑工业大学）；National University of Defense Technology（国防科技大学）；University of Warwick（华威大学）；Huazhong University of Science and Technology（华中科技大学）；Eindhoven University of Technology（埃因霍温理工大学）
University of Oxford（牛津大学）；Plexalis Ltd（Plexalis有限公司）
This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression, (b) radiological grading of common degenerative changes in intervertebral discs. Our contributions are as follows: (i) We propose a Spinal Context Transformer (SCT), a deep-learning architecture suited for the analysis of repeated anatomical structures in medical imaging such as vertebral bodies…
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, 39762, USA（工业与系统工程系，密苏里州立大学）；Department of Radiation Oncology, University of Mississippi Medical Center, Jackson, MS, 39216, USA（放射肿瘤学系，密苏里大学医学中心）
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
National Institutes of Health（美国国立卫生研究院）；National Library of Medicine（美国国家医学图书馆）；National Center for Biotechnology Information（国家生物技术信息中心）；Clinical Center（临床中心）；Radiology and Imaging Sciences（放射学与影像科学科）
The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. We previously developed a framework for detecting small BM (with diameters of less than 15mm) in T1-weighted Contrast-Enhanced 3D Magnetic Resonance images (T1c) to assist medical experts in this time-sensitive and high-stakes task. The framework utilizes a dedicated convolutional neural network (CNN) trained using labeled T1c data, where the ground truth BM segmentations were provided by a radiologist. This study aims to advance the framework with a noisy student-based …
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
The advent of digital pathology presents opportunities for computer vision for fast, accurate, and objective solutions for histopathological images and aid in knowledge discovery. This work uses deep learning to predict genomic biomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status, and intrinsic subtypes, from breast cancer histopathology images. Furthermore, we attempt to understand the underlying morphology as to how these genomic biomarkers manifest in images. Since gene sequencing is expensive, not always available, or even feasible, predicting these biomarkers fro…
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Ping An Insurance Company of China（中国平安保险（集团）股份有限公司）；Zhejiang Provincial People’s Hospital（浙江省人民医院）
Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists and prohibitive laboring costs motivated the automated approach. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. However, without voxel-level supervision, the performance is severely limited. LNS is highly context-dependent - LNS boundaries are constrained by anatomical organs - we formulate it as a deep spatial and contextual parsing problem via encoded anatomical organs. Thi…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest School of Medicine（维克森林大学医学院）
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow fo…
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；Centre Hospitalier Universitaire de Lyon（里昂大学医院中心）
Colorectal cancer is one of the most common and lethal cancers and colorectal cancer liver metastases (CRLM) is the major cause of death in patients with colorectal cancer. Multifocality occurs frequently in CRLM, but is relatively unexplored in CRLM outcome prediction. Most existing clinical and imaging biomarkers do not take the imaging features of all multifocal lesions into account. In this paper, we present an end-to-end autoencoder-based multiple instance neural network (AMINN) for the prediction of survival outcomes in multifocal CRLM patients using radiomic features extracted from cont…
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Stony Brook University（石溪大学）；Stony Brook Medicine（石溪医学院）；Stony Brook University Hospital（石溪大学医院）；University of Michigan Medical School（密歇根大学医学院）；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）；Emory University（埃默里大学）
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Segmentation of 3D High-frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut with Energy Functional Adapted to Local Intensity Distribution
NYU Tandon School of Engineering（纽约大学坦登工程学院）；Riverside Research（河畔研究公司）；University of Hawaii（夏威夷大学）；Kuakini Medical Center（库阿基尼医疗中心）
LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas
University of Lübeck（吕贝克大学）；Institute of Medical Informatics（医学信息学研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；German Aerospace Center（德国航空航天中心）
Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer
Shandong University（山东大学）；Hangzhou Dianzi University（杭州电子科技大学）；Communication University of Zhejiang（浙江传媒学院）；University of Leicester（莱斯特大学）；Sichuan Cancer Hospital and Institution（四川省肿瘤医院）；Sichuan Cancer Center（四川省癌症中心）；University of Electronic Science and Technology of China（电子科技大学）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）
LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients
Central South University（中南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Clinical Research Center for Breast Disease in Hunan Province（湖南省乳腺疾病临床研究中心）；The Chinese University of Hong Kong（香港中文大学）
Shiraz University（设拉子大学）；Koc University（科奇大学）；Shiraz University of Medical Sciences（设拉子医科大学）
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldwide, has been highlighted in the literature. Due to the indeterminable and subjective nature of Ki-67 as well as TILs scoring, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need consi…
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（中山大学附属肿瘤医院放射科）；Medical Imaging Center, Shenzhen Hospital of Southern Medical University（南方医科大学深圳医院医学影像中心）；Hithink RoyalFlush Information Network Co., Ltd.（慧思科技有限公司）；Department of Radiology, The third Affilliated Hospital of Guangzhou Medical University（广州医学院第三附属医院放射科）
ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
University of Amsterdam（阿姆斯特丹大学）；Netherlands Cancer Institute（荷兰癌症研究所）；Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；The Amsterdam University Medical Centre, AUMC, The University of Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹大学））；ZAS Hospitals（ZAS医院）；Peter Mac Callum Centre（彼得·麦卡勒姆中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
Can Physician Judgment Enhance Model Trustworthiness? A Case Study on Predicting Pathological Lymph Nodes in Rectal Cancer
National Cancer Center Research Institute（国立癌症中心研究所）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；National Cancer Center Hospital（国立癌症中心医院）；The University of Tokyo（东京大学）
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures
National Yang Ming Chiao Tung University（阳明交通大学）；National Taiwan University Cancer Center（台湾大学医学院附设癌医中心医院）；National Taiwan University Hospital（台湾大学医学院附设医院）；National Taiwan University Hsin-Chu Hospital（台湾大学医学院附设新竹医院）
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院（国家癌症中心/国家临床肿瘤医学中心）深圳医院）；Research Institute of Tsinghua, Guangzhou（清华大学广州研究院）
