Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images

Wahab, Noorul; Khan, Asifullah; Lee, Yeon Soo

doi:10.1093/jmicro/dfz002

Cited by 98 publications

(50 citation statements)

References 17 publications

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“…CNNs have been successfully applied to different ML related tasks; namely object detection, recognition, classification, regression, segmentation, etc., [182]- [184]. However, CNN generally needs a large amount of data for learning.…”

Section: Applications Of Cnnsmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

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Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown exemplary performance on several competitions related to Computer Vision and Image Processing. Interesting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, Speech Recognition, etc. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware technology have accelerated the research in CNNs, and recently very interesting deep CNN architectures have been reported. In fact, several interesting ideas to bring advancements in CNNs have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the major improvement in representational capacity of the deep CNN is achieved through architectural innovations. Especially, the idea of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing has gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges and applications of CNN are also provided. CNNs are the best among learning algorithms in understanding images content, and have shown exemplary results in segmentation, classification, detection, and retrieval related tasks [8], [9]. The success of CNNs has captured attention beyond academia. In industry, companies such as Google, Microsoft, AT&T, NEC, and Facebook have developed active research groups for exploring new architectures of CNN [10]. At present, most of the frontrunners of image processing and computer vision competitions are employing deep CNN based models.The attractive feature of CNN is its ability to exploit spatial or time correlation of the data. The topology of CNN is divided into multiple learning stages composed of a combination of the convolutional layers, non-linear processing units, and subsampling layers [11]. CNNs are feedforward multilayered hierarchical networks that are similar to fully connected neural network where each layer, using a bank of convolutional kernels, performs multiple transformations [12]. Convolution operation extracts useful features from locally correlated data points. Output of the convolutional kernels is assigned to non-linear processing unit (activation function), which not only helps in learning abstractions but also emb...

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Section: Applications Of Cnnsmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

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“…After the initial success of deep learning [ 10 ] in object recognition from images [ 3 , 11 ], deep neural networks have been adopted for a broad range of tasks in medical imaging, ranging from cell segmentation [ 12 ] and cancer detection [ 13 , 14 , 15 , 16 , 17 ] to intracranial hemorrhage detection [ 5 , 8 , 18 , 19 , 20 , 21 , 22 ] and CT/MRI super-resolution [ 23 , 24 , 25 , 26 ]. Since we address the task of intracranial hemorrhage detection, we consider related works that are focused on the same task as ours [ 5 , 6 , 7 , 8 , 18 , 19 , 20 , 21 , 22 , 27 , 28 , 29 , 30 ], as well as works that study intracranial hemorrhage segmentation [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Related Workmentioning

confidence: 99%

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Burduja

Ionescu

Verga

2020

Sensors

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In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

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“…In deep ANNs, transfer learning strategies are applied more frequently in the classification of breast histopathological images in recent four years. The papers involved in this article are [56], [57], [64], [65], [78], [82], [85], [86], [100], [106], [108], [112], [121], [125], [126], [129], [137], [155]. Transfer learning is a method used to transfer knowledge acquired from one task to resolve another [157].…”

Section: B Analysis Of Deep Ann Methodsmentioning

confidence: 99%

“…In [121], a transfer learning system based DCNN algorithm is suggested for the segmentation and detection of mitoses in breast cancer histopathological images. This system uses two CNNs.…”

Section: ) ''Tcug16'' Tasksmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images

Cited by 98 publications

References 17 publications

A survey of the recent architectures of deep convolutional neural networks

A survey of the recent architectures of deep convolutional neural networks

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

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