Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

Object detection is closely related with video and image analysis. Under computer vision technology, object detection model training with image-level labels only is challenging research area.Researchers have not yet discovered accurate model for Weakly Supervised Object Detection (WSOD). WSOD is used for detecting and localizing the objects under the supervision of image level annotations only.The proposed work usesself-paced approach which is applied on region proposal network of Faster R-CNN architecture which gives better solution from previous weakly-supervised object detectors and it can be applied for computer visionapplications in near future.

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Digital pathology 2.0: a deep learning image analysis tool to identify lung cancer in human tissue samples

10.26226/morressier.5b4709886f4cb300109520c8 ◽

2018 ◽

Author(s):

Patrick Micke

Keyword(s):

Lung Cancer ◽

Image Analysis ◽

Deep Learning ◽

Human Tissue ◽

Digital Pathology ◽

Analysis Tool ◽

Tissue Samples

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Deep learning for COVID-19 chest CT (computed tomography) image analysis: a lesson from lung cancer

10.21203/rs.3.rs-58467/v1 ◽

2020 ◽

Author(s):

Hao Jiang ◽

Shiming Tang ◽

Weihuang Liu ◽

Yang Zhang

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Image Analysis ◽

Deep Learning ◽

Diagnostic Testing ◽

Ct Images ◽

Chest Ct ◽

Learning Models ◽

Tomography Image ◽

Computed Tomography Image

Abstract As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, we build a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real CT images for COVID-19 and non-COVID-19 classification. In comparison, all models achieve excellent results (over than 90%) in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19.

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Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽

10.3390/cancers11111673 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1673 ◽

Cited By ~ 16

Author(s):

Shidan Wang ◽

Donghan M. Yang ◽

Ruichen Rong ◽

Xiaowei Zhan ◽

Junya Fujimoto ◽

...

Keyword(s):

Artificial Intelligence ◽

Lung Cancer ◽

Image Analysis ◽

Deep Learning ◽

Digital Pathology ◽

Diagnosis And Prognosis ◽

Model Interpretation ◽

Lung Cancer Diagnosis ◽

Cancer Pathology ◽

Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

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Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images

Modern Pathology ◽

10.1038/s41379-021-00987-4 ◽

2022 ◽

Author(s):

Xiaofeng Xie ◽

Chi-Cheng Fu ◽

Lei Lv ◽

Qiuyi Ye ◽

Yue Yu ◽

...

Keyword(s):

Neural Network ◽

Lung Cancer ◽

Deep Learning ◽

Pleural Effusion ◽

Convolutional Neural Network ◽

Cancer Cells ◽

Deep Convolutional Neural Network ◽

Learning Method ◽

Weakly Supervised

AbstractLung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019–9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.

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