Active convolutional neural networks for cancerous tissue recognition

Stanitsas, Panagiotis; Cherian, Anoop; Truskinovsky, Alexander M.; Morellas, Vassilios; Papanikolopoulos, Nikolaos

doi:10.1109/icip.2017.8296505

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…The assumption is that these examples will be especially informative during future training rounds, since they likely represent parts of the distribution of events that the model is unfamiliar with. This technique has been experimentally verified in many settings, including convolutional neural networks for image recognition [24].…”

Section: Classificationmentioning

confidence: 99%

Convolutional Neural Networks for Aircraft Noise Monitoring

Heller¹,

Anderson²,

Baker³

et al. 2018

Preprint

Self Cite

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Air travel is one of the fastest growing modes of transportation, however, the effects of aircraft noise on populations surrounding airports is hindering its growth. In an effort to study and ultimately mitigate the impact that this noise has, many airports continuously monitor the aircraft noise in their surrounding communities. Noise monitoring and analysis is complicated by the fact that aircraft are not the only source of noise. In this work, we show that a Convolutional Neural Network is well-suited for the task of identifying noise events which are not caused by aircraft. Our system achieves an accuracy of 0.970 when trained on 900 manually labeled noise events. Our training data and a TensorFlow implementation of our model are available at https://github.com/neheller/ aircraftnoise.

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Section: Classificationmentioning

confidence: 99%

Convolutional Neural Networks for Aircraft Noise Monitoring

Heller¹,

Anderson²,

Baker³

et al. 2018

Preprint

Self Cite

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“…Meanwhile, [5] uses deep Bayesian neural networks with monte-carlo dropout to identify the most uncertain samples for labelling. Furthermore, [22] uses the probability output of the convolutional neural network to label the instances based on discrete entropy and best-vs-second-best.…”

Section: Active Learningmentioning

confidence: 99%

QActor: On-line Active Learning for Noisy Labeled Stream Data

Younesian,

Zhao,

Ghiassi

et al. 2020

Preprint

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Noisy labeled data is more a norm than a rarity for selfgenerated content that is continuously published on the web and social media. Due to privacy concerns and governmental regulations, such a data stream can only be stored and used for learning purpose in a limited duration. To overcome the noise in this on-line scenario we propose QActor which novel combines: the selection of supposedly clean samples via quality models and actively querying an oracle for the most informative true labels. While the former can suffer from low data volumes of on-line scenarios, the latter is constrained by the availability and costs of human experts. QActor swiftly combines the merits of quality models for data filtering and oracle queries for cleaning the most informative data. The objective of QActor is to leverage the stringent oracle budget to robustly maximize the learning accuracy. QActor explores various strategies combining different query allocations and uncertainty measures. A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch. We extensively evaluate different image datasets fed into the classifier that can be standard machine learning (ML) models or deep neural networks (DNN) with noise label ratios ranging between 30% and 80%. Our results show that QActor can nearly match the optimal accuracy achieved using only clean data at the cost of at most an additional 6% of ground truth data from the oracle.

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“…Since a large number of medical images is difficult to obtain, other methods can be used to train the DNN. In [23], the authors used active learning to help with the dataset, that is, to help with selecting and classifying the images before training. They used multistage training scheme to overcome the overfitting problem, which means that they started with a smaller dataset and reduced it to the point where there is no overfitting.…”

Section: State Of the Art For Medical Imaging Classification Solutionsmentioning

confidence: 99%

“…To train and test our system, we used CT images of lungs that were previously classified by medical specialists and put into piles of yes/no (yes, the patient is diagnosed with lung cancer; and, no, the patient is cancer-free). Similar to Stanitsas and Cherian in [23], we pre-classified the images, but, in our case, we pre-classified them into groups of slice images taken from the same angle of the lung from different patients from our training dataset. Our system was trained using these images to be able to classify a new (previously unknown) image into one of the two piles (pile of cancer or pile of cancer-free) and tested the network to determine the success rate.…”

Section: State Of the Art For Medical Imaging Classification Solutionsmentioning

confidence: 99%

Using Double Convolution Neural Network for Lung Cancer Stage Detection

Jakimovski

Davčev

2019

Applied Sciences

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Recently, deep learning is used with convolutional Neural Networks for image classification and figure recognition. In our research, we used Computed Tomography (CT) scans to train a double convolutional Deep Neural Network (CDNN) and a regular CDNN. These topologies were tested against lung cancer images to determine the Tx cancer stage in which these topologies can detect the possibility of lung cancer. The first step was to pre-classify the CT images from the initial dataset so that the training of the CDNN could be focused. Next, we built the double Convolution deep Neural Network with max pooling to perform a more thorough search. Finally, we used CT scans of different Tx cancer stages of lung cancer to determine the Tx stage in which the CDNN would detect possibility of lung cancer. We tested the regular CDNN against our double CDNN. Using this algorithm, doctors will have additional help in early lung cancer detection and early treatment. After extensive training with 100 epochs, we obtained the highest accuracy of 0.9962, whereas the regular CDNN obtained only 0.876 accuracy.

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Active convolutional neural networks for cancerous tissue recognition

Cited by 10 publications

References 18 publications

Convolutional Neural Networks for Aircraft Noise Monitoring

Convolutional Neural Networks for Aircraft Noise Monitoring

QActor: On-line Active Learning for Noisy Labeled Stream Data

Using Double Convolution Neural Network for Lung Cancer Stage Detection

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