RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans

Abstract

We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sifts through 2D cross-sectional slices while paying close attention to potential hemorrhagic regions. Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level. We refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it employs original DenseNet architecture along with adding the components of attention for slice level predictions and recurrent neural network layer for incorporating 3D context. The real-world performance of RADnet has been benchmarked against independent analysis performed by three senior radiologists for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at CT level that is comparable to radiologists. Further, RADnet achieves higher recall than two of the three radiologists, which is remarkable.

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

“…Much interesting work has been performed for the automated ICH diagnosis. The majority of this work has focused either on a two-class detection problem where the method detects the presence of an ICH [6][7][8][9][10][11][12][13][14][15][16][16][17][18][19] or as a multi-class classification problem, where the goal is to detect the ICH sub-types [6,8,11,15,[17][18][19]. Some researchers have extended the scope and performed the ICH segmentation to identify the region of ICH [7,11,15,17,[19][20][21][22][23][24][25][26].…”

“…Some researchers have extended the scope and performed the ICH segmentation to identify the region of ICH [7,11,15,17,[19][20][21][22][23][24][25][26]. Most researchers validated their algorithms using small datasets [7][8][9][10][11][12][13]17,[20][21][22][24][25][26], while a few used large datasets for testing and validating [6,[14][15][16]18,19,23]. We provide a comprehensive review of the published papers for the ICH detection and segmentation ( Figure 1) in this section.…”

“…[19,23,25] , which were based on a FCN model. In these approaches, the spatial dependency between the adjacent slices was considered using a second model such as random forest [6] or RNN [13,18]. Some authors also modified CNN to process some part or the entire CT scan [14,15] or used an interpolation layer [17].…”

Computer-aided detection in chest radiography based on artificial intelligence: a survey

“…Recently, due to the extensive and successful application of deep learning in different image recognition tasks (such as image classification [5–8] and semantic segmentation [9–12]), interest has been stimulated in reapplying deep learning to medical images. In particular, advances in deep learning and large database construction have made the algorithm “go beyond” the performance of medical professionals in a variety of medical imaging tasks, including pneumonia diagnosis [13], diabetic retinopathy detection [14], skin cancer classification [15], arrhythmia detection [16], and bleeding identification [17]. Therefore, deep learning methods (especially CNN), which automatically learn image features to classify chest diseases, have become a mainstream trend.…”

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

“…We explored simpler feature pooling architectures (e.g, mean/max pooling), but each of these methods was outperformed by the LSTM in our experiments. The final hybrid CNN-LSTM architecture aligns with recent methods for state-of-the-art video classification [47,48] and 3D medical imaging [49].…”