Deep Convolutional Encoder-Decoders for Prostate Cancer Detection and Classification

Kiraly, Atilla P.; Nader, Clement Abi; Tüysüzoğlu, Ahmet; Grimm, Robert; Kiefer, Berthold; El-Zehiry, Noha; Kamen, Ali

doi:10.1007/978-3-319-66179-7_56

Cited by 30 publications

(42 citation statements)

References 6 publications

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“…Compared with the method, our method also shows higher recognition performance. This is because training a deep network is often complicated by convergence and overfitting issues with limited prostate cancer data.…”

Section: Resultsmentioning

confidence: 92%

“…While a vast body of research has been proposed to analyzing natural images, only a handful has dealt with the problem of prostate cancer classification with deep learning methods. [26][27][28][29][30][31] Reda et al 26 trained a stacked auto-encoder network with non-negativite constraint algorithm with a logistic regression classifier to distinguish the prostate tumor as either benign or malignant with ADC images. Kiraly et al 28 proposed multichannel image-to-image convolutional encoderdecoders to localize lesions and then output different tumor classes.…”

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

“…[26][27][28][29][30][31] Reda et al 26 trained a stacked auto-encoder network with non-negativite constraint algorithm with a logistic regression classifier to distinguish the prostate tumor as either benign or malignant with ADC images. Kiraly et al 28 proposed multichannel image-to-image convolutional encoderdecoders to localize lesions and then output different tumor classes. Chen et al 29 used the transfer learning approach with ImageNet pretrained inceptionV3 and Vgg-16 model to classify prostate cancers from mpMRI scans.…”

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

“…Recently, deep learning achieved inspiring performance in image classification with discriminative and representative features. While a vast body of research has been proposed to analyzing natural images, only a handful has dealt with the problem of prostate cancer classification with deep learning methods . Reda et al .…”

Section: Related Workmentioning

confidence: 99%

“…trained a stacked auto‐encoder network with non‐negativite constraint algorithm with a logistic regression classifier to distinguish the prostate tumor as either benign or malignant with ADC images. Kiraly et al . proposed multichannel image‐to‐image convolutional encoder–decoders to localize lesions and then output different tumor classes.…”

Section: Related Workmentioning

confidence: 99%

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Prostate cancer classification with multiparametric MRI transfer learning model

et al. 2019

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Purpose Prostate cancer classification has a significant impact on the prognosis and treatment planning of patients. Currently, this classification is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk free. This study aims to learn discriminative features in prostate images and assist physicians in classifying prostate cancer automatically. Methods We develop a novel multiparametric magnetic resonance transfer learning (MPTL) method to automatically stage prostate cancer. We first establish a deep convolutional neural network with three branch architectures, which transfer pretrained model to compute features from multiparametric MRI images (mp‐MRI): T2w transaxial, T2w sagittal, and apparent diffusion coefficient (ADC). The learned features are concatenated to represent information of mp‐MRI sequences. A new image similarity constraint is then proposed to enable the distribution of the features within the same category in a narrow angle region. With the joint constraints of softmax loss and image similarity loss in the fine‐tuning process, the MPTL can provide descriptive features with intraclass compactness and interclass separability. Results Two cohorts: 132 cases from our institutional review board‐approved patient database and 112 cases from the PROSTATEx‐2 Challenge are utilized to evaluate the robustness and effectiveness of the proposed MPTL model. Our model achieved high accuracy of prostate cancer classification (accuracy of 86.92%). Moreover, the comparison results demonstrate that our method outperforms both hand‐crafted feature‐based methods and existing deep learning models in prostate cancer classification with higher accuracy. Conclusion The experiment results showed that the proposed method can learn discriminative features in prostate images and classify the cancer accurately. Our MPTL model could be further applied in the clinical practice to provide valuable information for cancer treatment and precision medicine.

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Section: Resultsmentioning

confidence: 92%

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Prostate cancer classification with multiparametric MRI transfer learning model

et al. 2019

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Automatic Multiparametric Magnetic Resonance Imaging‐Based Prostate Lesions Assessment with Unsupervised Domain Adaptation

Dai

Wang

et al. 2023

Advanced Intelligent Systems

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Multiparametric magnetic resonance imaging (mpMRI) has emerged as a valuable diagnostic tool in prostate lesion assessment. However, training convolutional neural networks (CNNs) inevitably involves magnetic resonance (MR) images from multiple cohorts. There always exists variation in scanning protocol among cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Herein, a coarse mask‐guided deep domain adaptation network (CMD2A‐Net) is proposed to develop a fully automated framework for prostate lesion detection and classification (PLDC). No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion‐related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. Experiments are performed on 512 mpMRI sets from datasets of PROSTATEx (330 sets) and two cohorts, A (74 sets) and B (108 sets). Using ensemble learning, CMD2A‐Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large‐scale public dataset PROSTATEx to small‐scale target domains. Results from an ablation study also support its effectiveness in classification between benign and malignant lesions, compared to the state‐of‐the‐art models. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166081031.11420810/v1.

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Multi‐scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images

et al. 2022

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Purpose The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image‐guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi‐scale segmentation network with a cascading pyramid convolution module (CPCM) and a double‐input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI. Methods First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom‐up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM. Results For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15 mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64 mm. Conclusions The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state‐of‐the‐art methods.

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Deep Convolutional Encoder-Decoders for Prostate Cancer Detection and Classification

Cited by 30 publications

References 6 publications

Prostate cancer classification with multiparametric MRI transfer learning model

Prostate cancer classification with multiparametric MRI transfer learning model

Automatic Multiparametric Magnetic Resonance Imaging‐Based Prostate Lesions Assessment with Unsupervised Domain Adaptation

Multi‐scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images

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