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2021 - A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images.pdf

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2021 - A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images.pdf
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2021 - Connected-UNets a deep learning architecture for breast mass segmentation.pdf
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2021 - Breast Lesions Detection and Classification via YOLO-Based Fusion Models.pdf
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2021 - A framework for breast cancer classification using Multi-DCNNs.pdf
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2021 - A computer-aided diagnostic system for mammograms based on YOLOv3.pdf
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2020 - BR-GAN Bilateral Residual Generating Adversarial Network for Mammogram Classification.pdf
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2021 - Act_Like_a_Radiologist_Towards_Reliable_Multi-view_Correspondence_Reasoning_for_Mammogram_Mass_Detection.pdf
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2020 - Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning.pdf
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2021 - MommiNet-v2 Mammographic multi-view mass identification networks.pdf
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2020 - Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms.pdf
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2021 - Evaluation of U-net-based Image Segmentation Model to Digital Mammography.pdf
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2020 - Adversarial AutoAugment.pdf
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2021 - An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.pdf
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2022 - ARF-Net An Adaptive Receptive Field Network for breast mass segmentation in whole mammograms and ultrasound images.pdf
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2022 - Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques.pdf
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2020 - MultiResUNet Rethinking the U-Net architecture for multimodal biomedical image segmentation.pdf
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2020 - Attacks Which Do Not Kill Training Make Adversarial Learning Stronger.pdf
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2022 - An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks.pdf
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2020 - Deeply supervised U-Net for mass segmentation in digital mammograms.pdf
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2022 - A generative adversarial network for synthetization of regions of interest based on digital mammograms.pdf
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