Weakly-supervised learning for lung carcinoma classification using deep learning

Abstract

Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.

Phase separation of p53 precedes aggregation and is affected by oncogenic mutations and ligands

“…Image processing techniques have proven useful in addressing a range of problems. Successful applications, often in combination with other fields, have been implemented in diverse areas, ranging from vehicle tracking and traffic surveillance to computational pathology, 68 biomedicine 69 and chemistry. A specific example of the applications includes the use in the segmentation of abdominal CT images.…”

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

“…Out of two methodologies to train the simulations of 'fully supervised learning' and 'weakly supervised learning,' the last performed always superior with an improvement of 0.05 in the AUC on the experiment sets. 56 V.…”

A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy

“…Artificial intelligence based on a deep learning model that can assist the pathologists in evaluation of such difficult cases for diagnosis may be of great help. Deep learning models, especially convolutional neural networks (CNNs), have found numerous successful applications in the computational pathology 11 – 23 . One of the primary applications in histopathology is performing automatic cancer detection in whole-slide images (WSIs) 22 , 23 .…”

“…Deep learning models, especially convolutional neural networks (CNNs), have found numerous successful applications in the computational pathology 11 – 23 . One of the primary applications in histopathology is performing automatic cancer detection in whole-slide images (WSIs) 22 , 23 . However, as far as we are aware, there has not been any previous applications of deep learning to detect adenocarcinoma on pancreatic EUS-FNB specimens.…”

“…In this paper, we propose a deep learning model for pancreatic EUS-FNB WSI classification. We used a combination of transfer learning and fully-supervised learning to train an EfficientNet-B1 22 , 23 CNN on a dataset consisting of 372 WSIs. We then evaluated the model on a test set of 120 WSIs with a pathological diagnosis matched by three pancreatic pathologists, achieving high ROC AUC performance.…”