Data scarcity has refrained deep learning models from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature. The problem of small sample size was addressed and successfully tackled by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine, with imbalanced distribution among output categories taken into consideration. Model trained on public data set was able to generalize to data from an independent institution to make accurate prediction. The generated saliency map was found to overlay well with the lesion and could benefit clinicians for diagnosing purpose .
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification .
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Data scarcity has refrained
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying
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from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature. The problem of small sample size was addressed and successfully tackled by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine, with imbalanced distribution among output categories taken into consideration. Model trained on public data set was able to generalize to data from an independent institution to make accurate prediction. The generated saliency map was found to overlay well with the lesion and could benefit clinicians for diagnosing purpose
directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification