The Experts below are selected from a list of 54753 Experts worldwide ranked by ideXlab platform
Alexandr A Kalinin - One of the best experts on this subject based on the ideXlab platform.
-
ICIAR - Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Lecture Notes in Computer Science, 2018Co-Authors: Alexander Rakhlin, Andreevich Andrej Shvets, Vladimir Iglovikov, Alexandr A KalininAbstract:Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to disagreement between pathologists. Computer-aided diagnosis systems show potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image Classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class Classification Task, we report 87.2% accuracy. For 2-class Classification Task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image Classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.
-
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
bioRxiv, 2018Co-Authors: Alexander Rakhlin, Andreevich Andrej Shvets, Vladimir Iglovikov, Alexandr A KalininAbstract:Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image Classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class Classification Task, we report 87.2% accuracy. For 2-class Classification Task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image Classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.
Tomas Lopezsolaz - One of the best experts on this subject based on the ideXlab platform.
-
an approach to the use of word embeddings in an opinion Classification Task
Expert Systems With Applications, 2016Co-Authors: Fernando Enriquez, Jose A Troyano, Tomas LopezsolazAbstract:Vector-based word representations can help to improve a document classifier.The information of word2vec vectors and bags of words are very complementary.The combination of word2vec and BOW word representations obtains the best results.Word2vec is much more stable than bag of words models in cross-domain experiments. In this paper we show how a vector-based word representation obtained via word2vec can help to improve the results of a document classifier based on bags of words. Both models allow obtaining numeric representations from texts, but they do it very differently. The bag of words model can represent documents by means of widely dispersed vectors in which the indices are words or groups of words. word2vec generates word level representations building vectors that are much more compact, where indices implicitly contain information about the context of word occurrences. Bags of words are very effective for document Classification and in our experiments no representation using only word2vec vectors is able to improve their results. However, this does not mean that the information provided by word2vec is not useful for the Classification Task. When this information is used in combination with the bags of words, the results are improved, showing its complementarity and its contribution to the Task. We have also performed cross-domain experiments in which word2vec has shown much more stable behavior than bag of words models.
John V Guttag - One of the best experts on this subject based on the ideXlab platform.
-
patient risk stratification for hospital associated c diff as a time series Classification Task
Neural Information Processing Systems, 2012Co-Authors: Jenna Wiens, Eric Horvitz, John V GuttagAbstract:A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient's current or aggregate state. In this paper, we represent patient risk as a time series. In doing so, patient risk stratification becomes a time-series Classification Task. The Task differs from most applications of time-series analysis, like speech processing, since the time series itself must first be extracted. Thus, we begin by defining and extracting approximate risk processes, the evolving approximate daily risk of a patient. Once obtained, we use these signals to explore different approaches to time-series Classification with the goal of identifying high-risk patterns. We apply the Classification to the specific Task of identifying patients at risk of testing positive for hospital acquired Clostridium difficile. We achieve an area under the receiver operating characteristic curve of 0.79 on a held-out set of several hundred patients. Our two-stage approach to risk stratification outperforms classifiers that consider only a patient's current state (p<0.05).
Bryan Seybold - One of the best experts on this subject based on the ideXlab platform.
-
CNN architectures for large-scale audio Classification
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2017Co-Authors: Shawn Hershey, Sourish Chaudhuri, R. Channing Moore, Manoj Plakal, Devin Platt, Jort Florent Gemmeke, Aren Jansen, Rif A. Saurous, Daniel P.w. Ellis, Bryan SeyboldAbstract:Convolutional Neural Networks (CNNs) have proven very effective in image Classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image Classification do well on our audio Classification Task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) Classification Task.
Alexander Rakhlin - One of the best experts on this subject based on the ideXlab platform.
-
ICIAR - Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Lecture Notes in Computer Science, 2018Co-Authors: Alexander Rakhlin, Andreevich Andrej Shvets, Vladimir Iglovikov, Alexandr A KalininAbstract:Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to disagreement between pathologists. Computer-aided diagnosis systems show potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image Classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class Classification Task, we report 87.2% accuracy. For 2-class Classification Task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image Classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.
-
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
bioRxiv, 2018Co-Authors: Alexander Rakhlin, Andreevich Andrej Shvets, Vladimir Iglovikov, Alexandr A KalininAbstract:Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image Classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class Classification Task, we report 87.2% accuracy. For 2-class Classification Task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image Classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.
