Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images - the ACDC@LungHP Challenge 2019

AbstractDeep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. Treatment recommendations are predictions with an inherently causal interpretation. To use deep learning for these applications in the setting of observational data, deep learning methods must be made compatible with the required causal assumptions. We present a scenario with real-world medical images (CT-scans of lung cancer) and simulated outcome data. Through the data simulation scheme, the images contain two distinct factors of variation that are associated with survival, but represent a collider (tumor size) and a prognostic factor (tumor heterogeneity), respectively. When a deep network would use all the information available in the image to predict survival, it would condition on the collider and thereby introduce bias in the estimation of the treatment effect. We show that when this collider can be quantified, unbiased individual prognosis predictions are attainable with deep learning. This is achieved by (1) setting a dual task for the network to predict both the outcome and the collider and (2) enforcing a form of linear independence of the activation distributions of the last layer. Our method provides an example of combining deep learning and structural causal models to achieve unbiased individual prognosis predictions. Extensions of machine learning methods for applications to causal questions are required to attain the long-standing goal of personalized medicine supported by artificial intelligence.

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Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study

BMC Medicine ◽

10.1186/s12916-021-01953-2 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Huan Yang ◽

Lili Chen ◽

Zhiqiang Cheng ◽

Minglei Yang ◽

Jianbo Wang ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Intraclass Correlation ◽

Learning Model ◽

Normal Lung ◽

Histopathological Analysis ◽

Learning Methods ◽

Cell Lung Carcinoma ◽

Deep Learning Model ◽

Whole Slide Images

Abstract Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

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Detection Of Lung Cancer On Computed Tomography Using Artificial Intelligence Applications Developed By Deep Learning Methods And The Contribution Of Deep Learning To The Classification Of Lung Carcinoma

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210204210500 ◽

2021 ◽

Vol 17 ◽

Author(s):

Nevin Aydin ◽

Özer Çelik ◽

Ahmet Faruk Aslan ◽

Alper Odabaş ◽

Emine Dündar ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Squamous Cell ◽

Lung Carcinoma ◽

Small Cell Lung Carcinoma ◽

Small Cell ◽

Small Cell Lung ◽

Learning Methods ◽

Cell Lung Carcinoma ◽

Non Invasive

Background: In every year, lung cancer is an important cause of deaths in the world. Early detection of lung cancer is important for treatment, and non-invasive rapid methods are needed for diagnosis. Introduction: In this study, we aimed to detect lung cancer using deep learning methods and determine the contribution of deep learning to the classification of lung carcinoma using a convolutional neural network (CNN). Method: A total of 301 patients with diagnosed with lung carcinoma pathologies in our hospital were included in the study. In the thorax computed tomography (CT) performed for diagnostic purposes prior to treatment. After tagging the section images, tumor detection, small-non-small cell lung carcinoma differentiation, adenocarcinoma-squamous cell lung carcinoma differentiation, and adenocarcinoma-squamous cell-small cell lung carcinoma differentiation were sequentially performed using deep CNN methods. Result: : In total, 301 lung carcinoma images were used to detect tumors, and the model obtained with the deep CNN system had 0.93 sensitivity, 0.82 precision, and 0.87 F1 score in detecting lung carcinoma. In the differentiation of small cell-non-small cell lung carcinoma, the sensitivity, precision and F1 score of the CNN model at the test stage were 0.92, 0.65, and 0.76, respectively. In the adenocarcinoma-squamous cancer differentiation, the sensitivity, precision, and F1 score were 0.95, 0.80, and 0.86, respectively. The patients were finally grouped as small cell lung carcinoma, adenocarcinoma, and squamous cell lung carcinoma, and the CNN model was used to determine whether it could differentiate these groups. The sensitivity, specificity, and F1 score of this model were 0.90, 0.44, and 0.59, respectively for this differentiation. Conclusion.: In this study, we successfully detected tumors and differentiated between adenocarcinoma-squamous cell carcinoma groups with the deep learning method using the CNN model. Due to their non-invasive nature and success of the deep learning methods, they should be integrated into radiology to diagnose lung carcinoma.

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