To improve the model’s robustness and generalization performance, we investigate effective test-time augmentation-based ensemble prediction methods and evaluate the effectiveness of various ensemble prediction techniques in combination. In the training phase, we generate an optimized predictive model using a multi-modality regression network. The prediction is then determined through ensemble average voting with augmented test images generated by diverse data augmentation methods, including affine transformation, Mixup, Cutout, CutMix, and their combinations. Our experimentation reveal that all ensemble prediction methods demonstrated the ability to address issues through regularization, such as averaging errors on images subjected to random modifications. Notably, the use of Affine significantly improves over the baseline, with a 18.3% increase in accuracy and a 12.2% increase in AUC. The adoption of CutMix, maintains stability in both sensitivity and specificity, resulting in a higher balanced accuracy than Mixup and Cutout.
Assessment of prostate cancer aggressiveness is important because the effectiveness of treatment vary depending on the aggressiveness. The use of multi-parametric MR imaging prior to biopsy is recommended for accurate prostate cancer aggressiveness assessment but suffers from similar visual appearance of tumors between adjacent grades. To improve the predictive performance of prostate cancer aggressiveness, this study proposes a deep regression model involving size-normalized patch generation and multiple losses. First, we generate two types of input patches such as tumor-centered patch and size-normalized patch to effectively learn the characteristics of small tumors. Second, we propose a multiple loss functions consisting of triplet loss, mean squared error, and cross-entropy ordinal loss to increase the ability to discriminate between tumors with similar visual appearance and different aggressiveness. As a result, the proposed model trained with the size-normalized ADC map showed the highest performance with an accuracy of 78.85%, specificity of 89.66%, and AUC of 0.77. The ensemble model of tumor-centered T2w image and size-normalized ADC map improved sensitivity by 8.69% and showed the best performance with accuracy of 78.85%.
Recently, deep learning-based pneumonia classification has shown excellent performance in chest X-ray(CXR) images, but when analyzing classification results through visualization such as Grad-CAM, deep learning models have limitations in classifying by observing the outside of the lungs. To overcome these limitations, we propose a deep ensemble model with multiscale lung-focused patches for the classification of pneumonia. First, Contrast Limited Adaptive Histogram Equalization is applied to appropriately increase the local contrast while maintaining important features. Second, lung segmentation and multiscale lung-focused patches generation is performed to prevent pneumonia diagnosis from external lung region information. Third, we use a classification network with a Convolutional Block Attention Module to make the model to focus on meaningful regions and ensemble single models trained on large, middle and small-sized patches, respectively. For the evaluation of the proposed classification method, the model was trained on 5,216 pediatric CXRs and tested 624 images. Deep ensemble model trained on large and middle-sized patches showed the best performance with an accuracy of 92%, which is a 15%p improvement over the original single model.
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