Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs

Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are posed by the high variability of the cancerous tissue characteristics. In our experiments, state-of-the-art CNNs trained from scratch on histological images were less accurate and less robust to variability than a traditional machine learning framework, highlighting all the issues of fully training deep networks with limited data from real patients. To solve this problem, we designed and compared three transfer learning frameworks, leveraging CNNs pre-trained on non-medical images. This approach obtained very high accuracy, requiring much less computational resource for the training. Our findings demonstrate that transfer learning is a solution to the automated classification of histological samples and solves the problem of designing accurate and computationally-efficient CAD systems with limited training data.

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Enhanced Active Learning of Convolutional Neural Networks: A Case Study for Defect Classification in the Semiconductor Industry

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management ◽

10.5220/0010142902690276 ◽

2020 ◽

Author(s):

Georgios Koutroulis ◽

Tiago Santos ◽

Michael Wiedemann ◽

Christian Faistauer ◽

Roman Kern ◽

...

Keyword(s):

Neural Networks ◽

Active Learning ◽

Convolutional Neural Networks ◽

Semiconductor Industry ◽

Defect Classification

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Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz2816 ◽

2019 ◽

Vol 491 (2) ◽

pp. 1554-1574 ◽

Cited By ~ 5

Author(s):

Mike Walmsley ◽

Lewis Smith ◽

Chris Lintott ◽

Yarin Gal ◽

Steven Bamford ◽

...

Keyword(s):

Neural Networks ◽

Active Learning ◽

Galaxy Evolution ◽

Convolutional Neural Networks ◽

Time Scale ◽

Morphological Feature ◽

Learning Strategy ◽

Machine Intelligence ◽

Active Learning Strategy ◽

Support Research

ABSTRACT We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

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Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1714774 ◽

2020 ◽

Vol 41 (11) ◽

pp. 4057-4079 ◽

Cited By ~ 3

Author(s):

Guang Xu ◽

Xuan Zhu ◽

Nigel Tapper

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Active Learning ◽

Convolutional Neural Networks ◽

Large Scale ◽

Remote Sensing Images ◽

Target Searching

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The Histopathological Diagnosis of Adenocarcinoma & Squamous Cells Carcinoma of Lungs by Artificial intelligence: A comparative study of convolutional neural networks

10.1101/2020.05.02.20044602 ◽

2020 ◽

Author(s):

Mohammad Ali Abbas ◽

Syed Usama Khalid Bukhari ◽

Asmara Syed ◽

Syed Sajid Hussain Shah

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Convolutional Neural Networks ◽

Squamous Cell ◽

Digital Pathology ◽

Histopathological Diagnosis ◽

Data Set ◽

Diagnosis Of Lung Cancer

AbstractIntroductionMalignant tumors of the lung are the most important cause of morbidity and mortality due to cancer all over the world. A rising trend in the incidence of lung cancer has been observed. Histopathological diagnosis of lung cancer is a vital component of patient care. The use of artificial intelligence in the histopathological diagnosis of lung cancer may be a very useful technology in the near future.AimThe aim of the present research project is to determine the effectiveness of convolutional neural networks for the diagnosis of squamous cell carcinoma and adenocarcinoma of the lung by evaluating the digital pathology images of these cancers.Materials & MethodsA total of 15000 digital images of histopathological slides were acquired from the LC2500 dataset. The digital pathology images from lungs are comprised of three classes; class I contains 5000 images of benign lung tissue, class II contains 5,000 images of squamous cell carcinoma of lungs while Class III contains 5,000 images of adenocarcinoma of lungs. Six state of the art off the shelf convolutional neural network architectures, VGG-19, Alex Net, ResNet: ResNet-18, ResNet-34, ResNet-50, and ResNet-101, are used to assess the data, in this comparison study. The dataset was divided into a train set, 55% of the entire data, validation set 20%, and 25% into the test data set.ResultsA number of off the shelf pre-trained (on ImageNet data set) convolutional neural networks are used to classify the histopathological slides into three classes, benign lung tissue, squamous cell carcinoma-lung and adenocarcinoma - lung. The F-1 scores of AlexNet, VGG-19, ResNet-18, ResNet-34, ResNet-50 and ResNet-101, on the test dataset show the result of 0.973, 0.997, 0.986, 0.992, 0.999 and 0.999 respectively.DiscussionThe diagnostic accuracy of more 97% has been achieved for the diagnosis of squamous cell carcinoma and adenocarcinoma of the lungs in the present study. A similar finding has been reported in other studies for the diagnosis of metastasis of breast carcinoma in lymph nodes, basal cell carcinoma, and prostatic cancer.ConclusionThe development of algorithms for the recognition of a specific pattern of the particular malignant tumor by analyzing the digital images will reduce the chance of human errors and improve the efficiency of the laboratory for the rapid and accurate diagnosis of cancer.

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