使用CNN进行肺癌患者的基于图像的生存分析

基于深度卷积神经网络的肺癌诊断

A Deep Convolutional Neural Network for Lung Cancer Diagnostic

在本文中，我们研究了深度学习技术在医学图像分析问题上诊断肺癌的优势。卷积神经网络（CNN）模型由于其在生成高层次图像表示方面的良好效果而成为模式识别和计算机视觉研究领域的热门。我们提出了一种新的深度学习架构，用于学习高级图像表示，以在医学图像二值分类任务中实现低方差的高分类精度。我们的目标是在深度卷积神经网络的开始学习判别紧凑特征。我们在Kaggle Data Science Bowl 2017（KDSB17）数据集上评估了我们的模型，并将其与Kaggle竞赛中提出的一些相关作品进行了比较。

基础模型与放射组学在肺部计算机断层扫描中的比较：特征提取器、分类头和分割选择的基准测试

Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices

本研究通过基准测试五种特征提取器、七种分类头和三种分割策略，评估基础模型与放射组学在CT肺癌表型分析中的性能，发现最优设计取决于任务，推荐Curia结合肿瘤分割和CatBoost分类头作为安全默认方案。

多模态生存预测中缺失模态的处理：非小细胞肺癌的应用

Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer

本文提出一种缺失感知的多模态生存框架，结合CT、WSI和临床数据，通过基础模型提取特征并融合，提升NSCLC生存预测的准确性与临床适用性。

Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning

Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopathological or morphological features indicating the risks of recurrence and metastasis in LSCC. Our study focuses on the recurrence prediction of LSCC based on H&E-stained histopathological whole-slide images (WSI). Due to the small size of LSCC cohorts in terms of patients with available recurrence information, standard end-to-end learning with various convolutional neural networks for this task tends to overfit. Al…

MSAD-Net：用于肺癌分类的多尺度和空间注意力密集网络

MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification

提出MSAD-Net，一种带空间注意力的多尺度密集CNN，通过新颖密集模块和跳跃连接解决类别不平衡，在肺癌分类上显著超越现有模型。

基于最大强度投影的卷积神经网络在CT肺结节自动检测中的应用

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

准确的肺结节检测是肺癌筛查的关键步骤。尽管计算机辅助检测（CAD）系统具有潜在的优势，但放射科医师在临床实践中并不常规使用CAD系统进行肺结节检测。最大强度投影（MIP）图像可提高计算机断层扫描（CT）放射学评价中肺结节的检出率。受放射科医生临床方法学的启发，我们的目标是探索应用MIP图像来提高使用卷积神经网络（CNN）自动肺结节检测的有效性的可行性。我们提出了一种基于CNN的方法，以MIP图像的不同板厚（5毫米，10毫米，15毫米）和1毫米的轴向截面切片作为输入。这种方法用更有代表性的空间信息增强了二维（2-D）CT切片图像，有助于通过其形态区分结节和血管。我们提出的方法在LIDC-IDRI数据集中的888次扫描中实现了92.67%的灵敏度和94.19%的灵敏度，每次扫描有1个假阳性，每次扫描有2个假阳性。使用厚MIP图像有助于检测小的肺结节（3 mm-10 mm），并减少假阳性。实验结果表明，利用MIP图像可以提高灵敏度和降低假阳性的数量，这证明了所提出的基于MIP的CNN框架在CT扫描中自动检测肺结节的有效性和意义。所提出的方法还显示了CNN通过结合临床程序可以获得结节检测益处的潜力。

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data

Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging. While most studies deploy cross-entropy as the loss function in such tasks, a growing number of approaches have turned to a family of contrastive learning-based losses. Even though performance metrics such as accuracy, sensitivity and specificity are regularly used for the evaluation of CNN classifiers, the features that these classifiers actually learn are rarely identified and their effect on the classification performance on out-of-distribution test samples is ins…

Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)

Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. Cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining…

Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels

Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases. We tackle this problem …

用于多模态肺癌生存预测的CT-CLIP表示

CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction

研究评估特定领域基础模型CT-CLIP用于肺癌多模态生存预测，通过多种策略提取特征，结果显示冻结的CT-CLIP模型结合可训练生存头表现出色，能有效区分高低风险组。

使用 VGG16 算法对 CT 扫描图像中的肺癌进行分类

Using VGG16 Algorithms for classification of lung cancer in CT scans Image

本文提出使用 VGG16 深度学习算法对 CT 扫描图像中的肺结节进行恶性、良性和健康分类，实现了 92.08% 的灵敏度、91% 的准确率和 93% 的 AUC。

基于卷积神经网络的多源迁移学习肺模式分析

Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis

间质性肺病的早期诊断对于其治疗至关重要，但即使是有经验的医生也很难发现，因为它们的临床表现相似。为了辅助诊断，已经开发了计算机辅助诊断（CAD）系统。这些通常依赖于扫描CT图像、识别纹理肺模式并生成病理图的固定尺度分类器。在之前的研究中，我们提出了一种使用深度卷积神经网络（CNN）对肺组织模式进行分类的方法，该方法具有针对特定问题设计的架构。在这项研究中，我们提出了一种改进的方法来训练所提出的网络从一般纹理分类的相似域的知识转移。使用六个公开可用的纹理数据库来预训练具有拟议架构的网络，然后根据肺组织数据进行微调。由此产生的CNN被组合在一个集成中，它们的融合知识被压缩回具有原始架构的网络。所提出的方法导致所提出的CNN的性能绝对增加约2%。结果表明，迁移学习在医学图像分析领域的潜力，表明问题的纹理性质，并表明用于训练网络的方法可以作为重要的设计其架构。

Resource-Frugal Classification and Analysis of Pathology Slides Using Image Entropy

Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices. In particular, the challenging task of distinguishing adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC) lung cancer subtypes is approached in two stages. First, whole-slide histopathology images are downsampled to a size too large for CNN analysis but large enough to retain key anatomic detail. The downsampled images are decomposed into smaller square tiles, which are sifted based on their image entropies. A lightweight CNN produces tile-l…

磁共振delta影像组学用于追踪接受立体定向MRI引导放疗的肺部肿瘤的放射反应

Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy

本研究通过回顾性分析47次MR引导肺部SBRT治疗，验证了delta影像组学的可行性与稳定性，发现偏度等特征可追踪放射剂量反应并与局部复发相关，为个体化放疗剂量调整提供依据。

Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays

Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very sma…

快速CapsNet用于肺癌筛查

Fast CapsNet for Lung Cancer Screening

肺癌是过去几年中癌症相关死亡的主要原因。肺癌筛查的一个主要挑战是从计算机断层扫描（CT）扫描中检测肺结节。自动肺结节分类的最新方法使用深卷积神经网络（CNN）。然而，这些网络需要大量的训练样本才能很好地泛化。本文研究了使用胶囊网络（CapsNets）作为CNN的替代方案。我们发现，当训练样本数量较少时，CapsNets的性能明显优于CNN。为了提高计算效率，我们提出了一个一致的动态路由机制，导致$3\times$ CapsNet的加速。最后，我们表明CapNets的原始图像重建方法在肺结节数据上表现不佳。我们提出了一个有效的替代方案，称为卷积解码器，产生较低的重建误差和较高的分类精度。

In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays

Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to ind…

深度学习与经典回归用于脑肿瘤患者生存预测

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

医学成像数据回归任务的深度学习已经显示出有希望的结果。然而，与其他方法相比，它们的能力与数据集大小密切相关。在这项研究中，我们评估了3D-卷积神经网络（CNN）和经典回归方法与手工制作的功能，用于高级别脑肿瘤患者的生存时间回归。用于回归的测试CNN显示出有希望但不稳定的结果。表现最好的深度学习方法在训练集的保留样本上达到了51.5%的准确率。所有测试的深度学习实验都优于使用30个放射组学特征的支持向量分类器（SVC）。研究的特征包括强度、形状、位置和深度特征。提交给BraTS 2018生存预测挑战的方法是一个SVC集合，在BraTS 2018训练集上达到了72.2%的交叉验证准确率，在验证集上达到了57.1%，在测试集上达到了42.9%。结果表明，更多的训练数据是必要的CNN模型从磁共振图像直接回归的稳定性能，以及非成像临床患者信息是至关重要的成像信息。

Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification

Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists, ultimately allowing for better medical care of lung-, heart- and chest-related conditions. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information that is typically lost in the down-sampling …

从细胞到生存：多乘 microscopy 中细胞互相关系的分层分析用于肺癌预后

From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis

HiGINE通过多光谱免疫荧光图像分析肿瘤微环境，利用分层图方法预测肺癌患者生存并提升风险分层。

MESAHA-Net：基于多编码器的自适应硬注意力网络与最大强度投影用于CT扫描中肺结节分割

MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan

提出MESAHA-Net，一种多编码器自适应硬注意力网络，融合CT切片、最大强度投影和ROI掩膜，在LIDC-IDRI上实现高精度且高效的肺结节三维分割，优于现有方法。

利用深度学习与CT成像改进肺癌诊断与生存预测

Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging

本研究针对肺癌早诊与生存预测需求，提出结合扩展Cox模型的小批量损失与二元交叉熵的卷积神经网络方法，在国家肺癌筛查试验数据集上实现了肺癌分类与生存预测的高性能，为肺癌诊疗优化提供了新路径。

Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study

Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics and LIDC-IDRI - as test datasets for two tasks - pre-treatment s…

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analys…

LNDb：一个基于CT的肺部诊断数据库

LNDb: A Lung Nodule Database on Computed Tomography

肺癌是世界上最致命的癌症类型，晚期发现是患者存活率低的主要因素。低剂量计算机断层扫描已被建议作为一种潜在的筛选工具，但手动筛选是昂贵的，耗时的，易于变化。这推动了用于肺结节的检测、分割和表征的自动方法的发展，但其在临床常规中的应用是具有挑战性的。在这项研究中，一个新的数据库的开发和测试肺结节计算机辅助策略，旨在补充目前的数据库，给予额外的重点放射科医生的变异性和当地的临床现实。国家的最先进的结节检测，分割和表征方法进行了测试和比较，手动注释，以及多个放射科医生和放射科医生和计算机辅助系统相结合的协作策略。研究表明，最先进的方法可以像放射科医生一样准确地确定患者的随访建议，尽管所使用的结节检测方法在该数据库中显示性能下降。

基于Vision Transformers的肺部疾病检测：机器学习方法的比较研究

Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods

本研究探索Vision Transformers在胸部X光分析中的应用，比较了全图与肺部分割两种方法，发现ViT无需显式分割即可超越传统CNN，显著提升诊断准确性。

Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks

Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection and earlier diagnoses, there are still significant challenges in treating the disease. In particular, despite an increased number of curative resections, many postoperative patients still develop recurrent lesions. Consequently, there is a significant need for prognostic tools that can more accurately predict a patient's risk for recurrence. In this paper, we explore the use of convolutional neural networks (CNNs) fo…

基于2D PET/CT投影的时间条件与多时间生存预测在肺癌中的应用

Time-Conditioned and Multi-Time Survival Prediction from 2D PET/CT Projections in Lung Cancer

提出时间条件生存（ATCS）和多时间生存（MTS）两种方法，利用2D PET/CT投影预测非小细胞肺癌患者生存，ATCS在早期预测更优，MTS在晚期更优。

用于CT扫描中肺部病灶分类的门控扩张网络

Gated-Dilated Networks for Lung Nodule Classification in CT scans

不同类型的卷积神经网络（CNN）已被应用于从计算机断层摄影（CT）扫描中检测癌性肺结节。然而，结节的大小是非常多样化的，可以在3到30毫米之间的任何地方。结核大小差异很大，使其分类成为一项困难和具有挑战性的任务。在这项研究中，我们提出了一种新的CNN架构，称为门控扩张（GD）网络，将结节分类为恶性或良性。与以前的研究不同，GD网络使用多个扩张卷积而不是最大池来捕获尺度变化。此外，GD网络具有上下文感知子网络，其分析输入特征并将特征引导到合适的扩张卷积。我们在来自LIDC-LDRI数据集的1,000多个CT扫描上评估了所提出的网络。我们提出的网络优于最先进的基线模型，包括Multi-Crop，Resnet和Densenet，AUC>0.95。与基线模型相比，全球动力学网络提高了中等大小结核的分类准确度。此外，我们观察到结节的大小和上下文感知子网络产生的注意信号之间的关系，这验证了我们的新网络架构。

卷积神经网络结构在孤立性肺结节恶性程度自动表征中的实验研究

Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating

肺癌是全球癌症相关死亡的最常见原因。在计算机断层扫描（CT）胸部扫描中早期自动诊断孤立性肺结节（SPN）可以提供早期治疗，并将医生从耗时的程序中解放出来。深度学习已被证明是许多医学成像诊断领域中流行和有影响力的方法。在这项研究中，我们考虑的问题，良性和恶性肺结节的CT图像来自PET/CT扫描仪之间的诊断分类。更具体地说，我们打算开发实验性卷积神经网络（CNN）架构，并通过调整其参数来进行实验，以研究其行为，并定义准确分类的最佳设置。对于实验，我们利用从帕特雷大学核医学实验室获得的PET/CT图像，以及医学上可用的数据库，称为肺图像数据库联盟图像集（LIDC-IDRI）。此外，我们应用简单的数据增强来生成新的实例，并检查所开发的网络的性能。相应地，在PET/CT数据集上和在来自LIDC-IDRI数据集的结节图像的选择上实现了91%和93%的分类准确度。结果表明，CNN是一个值得信赖的方法，结节分类。此外，实验证实，数据增强增强了CNN的鲁棒性。

基于医学图像的深度学习分类模型用于肺癌与肺部疾病

Classification based deep learning models for lung cancer and disease using medical images

本文提出基于ResNet改进的ResNet+模型，通过引入ResNet-D模块和卷积注意力机制增强特征提取与泛化能力，在多个肺癌及肺部疾病公开数据集上取得优于基线模型的准确率并降低计算成本。

Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks

Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting CXR in adults. However, there is a lack of evidence indicating that D-CNNs can recognize accurately multiple lung pathologies from pediatric CXR scans. In particular, the development of diagnostic models for the detection of pediatric chest diseases faces significant challenges such as (i) lack of physician-annotated datasets and (ii) class imbalance …

Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images

The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-th…

MIXCAPS: A Capsule Network-based Mixture of Experts for Lung Nodule Malignancy Prediction

Lung diseases including infections such as Pneumonia, Tuberculosis, and novel Coronavirus (COVID-19), together with Lung Cancer are significantly widespread and are, typically, considered life threatening. In particular, lung cancer is among the most common and deadliest cancers with a low 5-year survival rate. Timely diagnosis of lung cancer is, therefore, of paramount importance as it can save countless lives. In this regard, deep learning radiomics solutions have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated b…

儿童肺炎从胸部X光片检测：迁移学习与定制CNNs的比较研究

Pediatric Pneumonia Detection from Chest X-Rays:A Comparative Study of Transfer Learning and Custom CNNs

本研究通过比较迁移学习与定制CNNs在儿童肺炎检测中的性能，发现微调迁移模型在准确率、F1分数和AUC上均优于从头训练模型，具有在资源有限地区应用的潜力。

使用 Faster R-CNNs 进行肺部疾病图像分割

Lung Diseases Image Segmentation using Faster R-CNNs

本文提出一种低密度Faster R-CNNs神经网络结构，通过特征金字塔和Soft Non-Maximal Suppression优化区域提议，在胸部X光图像上实现了高效的肺部疾病分割与检测。

通过多模态表示学习进行肺癌生存预测

Survival Prediction in Lung Cancer through Multi-Modal Representation Learning

本文提出一种融合CT、PET和基因组数据的多模态表示学习方法，通过自监督学习和跨患者模块对齐相似患者嵌入，提升非小细胞肺癌生存预测性能。

Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review

This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmen…

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images

Novel Coronavirus disease (COVID-19) is an extremely contagious and quickly spreading Coronavirus infestation. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep Convolutional Neural Networks (CNNs). A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition syste…

Multimodal Learning for Non-small Cell Lung Cancer Prognosis

This paper focuses on the task of survival time analysis for lung cancer. Although much progress has been made in this problem in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep learning-based survival time analyses for lung cancer are mostly based on textual clinical information such as staging, age, histology, etc. Unlike existing methods that predicting on the single modality, we observe that a human clinician usually takes multimodal data such as text clinical data and visual scans to estimate survival time. Motivated by this, in …

用于非小细胞肺癌（NSCLC）患者生存预测的基于跨模态注意力的多模态融合

Cross-modality Attention-based Multimodal Fusion for Non-small Cell Lung Cancer (NSCLC) Patient Survival Prediction

本文提出基于跨模态注意力的多模态融合流程，通过建模模态间关系整合病理图像与 RNA-seq 信息，提升 NSCLC 患者生存预测性能。

通过二维卷积神经网络和肺部CT扫描进行磨玻璃影的严重程度分类：为期3天的探索

Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration

本研究在3天内构建并测试了基于2D CNN的肺部CT磨玻璃影严重程度分类框架，通过微调DenseNet、ResNet等模型并使用ADAM优化，发现部分2D CNN性能可与ResNet结合RNN的架构相媲美。

用于肺结节弱监督分割的CNN鉴别性定位

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules

在肺部计算机断层扫描（CT）扫描上自动检测和分割肺结节可以促进早期肺癌诊断。用于CT扫描上的自动结节分割的现有监督方法需要用于训练的基于体素的注释，这是耗费人力和时间的。在这项工作中，我们提出了一种弱监督方法，该方法仅使用图像级标签进行训练，生成准确的体素级结节分割。通过调整为图像分类训练的卷积神经网络（CNN），我们所提出的方法从不同尺度的卷积单元的激活图中学习有区别的区域，并使用新的候选筛选框架来识别真正的结节位置。在公共LIDC-IDRI数据集上的实验结果表明，与基于CNN的全监督分割方法相比，我们的弱监督结节分割框架具有竞争力的性能。

Deep Learning Approach for Early Stage Lung Cancer Detection

Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode a…

xCG：用于非小细胞肺癌生存预测的可解释细胞图

xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer

提出一种可解释细胞图（xCG）方法，利用图神经网络对肿瘤微环境进行空间建模，结合成像质谱流式数据预测非小细胞肺癌患者生存期，并通过逐层相关性传播方法在细胞水平提供风险归因解释。

用于基于治疗前CT的多模态非小细胞肺癌（NSCLC）生存预测的结构化代理特征

Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT

该研究针对NSCLC生存分层难题，用模拟衍生的6种结构化代理特征扩充多模态数据，结合TMAE模型，在Lung1队列取得优于现有方法的预测性能，验证了代理特征的互补价值。

Effect of Input Size on the Classification of Lung Nodules Using Convolutional Neural Networks

Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. The number of slices in a CT scan can be up to 600. Therefore, computer-aided-detection (CAD) systems are very important for faster and more accurate assessment of the data. In this study, we proposed a framework that analyzes CT lung screenings using convolutional neu…

Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data

Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of progression. CT scans of the lungs inform clinical assessment of IPF patients and contain pertinent information related to disease progression. In this work, we propose a multi-modal method that uses neural networks and memory banks to predict the survival of IPF patients using clinical and imaging data. The majority of clinical IPF patient records have missing data (e.g. missing lung function tests). To this end, we propose a probabilistic model that captures the de…

Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs

We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in orde…

深度分割网络预测非小细胞肺癌的生存率

Deep segmentation networks predict survival of non-small cell lung cancer

非小细胞肺癌（NSCLC）占肺癌诊断的约80-85%，是全球癌症相关死亡的主要原因。最近的研究表明，正电子发射断层扫描-计算机断层扫描（PET/CT）图像的基于图像的放射组学特征对NSCLC结局具有预测能力。为此，容易计算的功能特征，如标准摄取值（SUV）和总病变糖酵解（TLG）的最大值和平均值最常用于NSCLC的预测，但其预后价值仍存在争议。与此同时，卷积神经网络（CNN）正迅速成为癌症图像分析的新前提，与其他手工制作的放射组学特征相比，其预测能力显著增强。在这里，我们展示了经过训练的CNN执行肿瘤分割任务，除了医生轮廓之外没有其他信息，识别出一组丰富的与生存相关的图像特征，具有显著的预后价值。在一项针对96例接受立体定向体部放疗（SBRT）的NSCLC患者的回顾性研究中，我们发现，针对PET/CT图像中的肿瘤分割进行训练的CNN分割算法（U-Net）包含与2年和5年总体和疾病特异性生存率具有强相关性的特征。除了医生提供的图像和相应的肿瘤轮廓外，U-net算法没有看到任何其他临床信息（例如生存、年龄、吸烟史）。此外，通过对U-Net的可视化，我们还发现了令人信服的证据，表明进展区域似乎与U-Net特征识别出预测死亡可能性较高的模式的区域相匹配。我们预计我们的研究结果将成为更复杂的非侵入性患者特异性癌症预后确定的起点。

基于知识的CT图像死亡率预测分析

Knowledge-based Analysis for Mortality Prediction from CT Images

最近的研究强调了心血管疾病（CVD）和肺癌之间的高度相关性，两者都与显著的发病率和死亡率相关。低剂量CT（LCDT）扫描显著提高了肺癌诊断的准确性，从而降低了癌症死亡率。然而，肺癌和CVD之间的高度相关性尚未被很好地用于死亡率预测。本文介绍了一种基于知识的分析方法，使用深度卷积神经网络（CNN）进行全因死亡率预测。该方法结合了从CNN中提取的结构图像特征，基于不同尺度的LDCT体积，以及从定量测量中获得的临床知识，以综合预测肺癌筛查受试者的死亡风险。介绍的方法在这里被称为基于知识的死亡率预测网络分析，或KAMP-Net。它构成了利用成像特征和解剖信息的协作框架，而不是完全依赖于自动特征提取。我们的工作证明了结合定量临床测量来帮助CNN从胸部LDCT图像预测全因死亡率的可行性。本研究的结果证实，放射科医生定义的特征是CNN的重要补充，以实现更全面的特征提取。因此，所提出的KAMP-Net与其他方法相比具有更好的性能。我们的代码可在www.example.com上获得。

低剂量CBCT图像放射组学新特征用于肺癌患者生存预测

Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images

预测患者的肿瘤进展存活率可用于估计治疗方案的有效性。在我们的工作中，我们提出了一个模型，考虑到肿瘤的异质性来预测生存。通过将图像中获得的放射密度信息与总肿瘤体积（GTV）相结合，根据肿瘤质量测量肿瘤异质性。我们提出了一个新的功能，称为肿瘤质量内的GTV（TMG），提高了生存能力的预测，相比现有的模型，使用GTV。患者TMG的每周变化是根据图像数据计算出来的，也是根据细胞存活率模型估计出来的。从细胞存活率模型获得的参数是治疗期间TMG变化的指标。我们使用这些参数以及其他患者元数据来执行生存分析和回归。使用这些数据进行Cox比例风险生存回归。当在模型中使用TMG而不是GTV时，观察到平均一致性指数从0.47显著改善至0.64。实验表明，有反应和无反应的患者的治疗反应存在差异，所提出的方法可以用来预测患者的生存能力。

Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models

Lesion segmentation is a crucial step of the radiomic workflow. Manual segmentation requires long execution time and is prone to variability, impairing the realisation of radiomic studies and their robustness. In this study, a deep-learning automatic segmentation method was applied on computed tomography images of non-small-cell lung cancer patients. The use of manual vs automatic segmentation in the performance of survival radiomic models was assessed, as well. METHODS A total of 899 NSCLC patients were included (2 proprietary: A and B, 1 public datasets: C). Automatic segmentation of lung le…

深度卷积神经网络用于直肠癌影像学生存分析

Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer

最近的放射组学研究已经见证了深度学习技术在学习放射组学特征和融合多模态成像数据方面的良好表现。大多数现有的基于深度学习的放射组学研究在模式分类的设置中建立预测模型，不适合生存分析研究，其中一些数据样本具有不完整的观察结果。为了改善现有的生存分析技术，其性能取决于成像特征，我们提出了一种深度学习方法，通过在比例风险模型中使用深度卷积神经网络（CNN）优化成像特征来构建生存回归模型。为了使CNN适用于不同大小的肿瘤，采用了空间金字塔池化策略。我们的方法已被验证的基础上模拟成像数据集和FDG-PET/CT数据集的直肠癌患者治疗局部晚期直肠癌。与基于Cox比例风险模型和随机生存森林的手工放射组学特征构建的生存预测模型相比，该方法具有竞争力的预测性能。