Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection
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Springer
Materia
Attention-based multiple instance learning Variational Gaussian processes CT hemorrhage detection
Fecha
2021-07-04Referencia bibliográfica
Published version: Wu Y... [et al.] (2021) Combining Attention-Based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection. In: de Bruijne M. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12902. Springer, Cham. [https://doi.org/10.1007/978-3-030-87196-3_54]
Patrocinador
European Commission 860627; Spanish Government PID2019-105142RB-C22Resumen
Intracranial hemorrhage (ICH) is a life-threatening emer-
gency with high rates of mortality and morbidity. Rapid and accurate de-
tection of ICH is crucial for patients to get a timely treatment. In order to
achieve the automatic diagnosis of ICH, most deep learning models rely
on huge amounts of slice labels for training. Unfortunately, the manual
annotation of CT slices by radiologists is time-consuming and costly. To
diagnose ICH, in this work, we propose to use an attention-based multiple
instance learning (Att-MIL) approach implemented through the combi-
nation of an attention-based convolutional neural network (Att-CNN)
and a variational Gaussian process for multiple instance learning (VGP-
MIL). Only labels at scan-level are necessary for training. Our method
(a) trains the model using scan labels and assigns each slice with an at-
tention weight, which can be used to provide slice-level predictions, and
(b) uses the VGPMIL model based on low-dimensional features extracted
by the Att-CNN to obtain improved predictions both at slice and scan
levels. To analyze the performance of the proposed approach, our model
has been trained on 1150 scans from an RSNA dataset and evaluated
on 490 scans from an external CQ500 dataset. Our method outperforms
other methods using the same scan-level training and is able to achieve
comparable or even better results than other methods relying on slice-
level annotations.