A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images
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[1] Jiahong Dong,et al. Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery , 2022, Frontiers in Oncology.
[2] Yitian Xu,et al. CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images , 2022, International Journal of Machine Learning and Cybernetics.
[3] B. Garcia-Zapirain,et al. Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network , 2022, Scientific Reports.
[4] Chengzhang Zhu,et al. MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma , 2022, Cancers.
[5] Linyan Xue,et al. Automatic polyp detection and segmentation using shuffle efficient channel attention network , 2021 .
[6] Shyam Lal,et al. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images , 2021, International Journal of Computer Assisted Radiology and Surgery.
[7] A. Jemal,et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.
[8] Qing-Long Zhang,et al. SA-Net: Shuffle Attention for Deep Convolutional Neural Networks , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[9] Anirudh Kanfade,et al. NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images , 2020, Comput. Biol. Medicine.
[10] Saeed Hassanpour,et al. Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides , 2020, Scientific Reports.
[11] S. Gelly,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.
[12] Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. , 2020, CA: a cancer journal for clinicians.
[13] Zhebin Du,et al. Trends and projections of kidney cancer incidence at the global and national levels, 1990–2030: a Bayesian age-period-cohort modeling study , 2020, Biomarker Research.
[14] Zhebin Du,et al. Trends and projections of kidney cancer incidence at the global and national levels, 1990–2030: a Bayesian age-period-cohort modeling study , 2020, Biomarker Research.
[15] Zafer Cömert,et al. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer , 2020 .
[16] C. V. Jawahar,et al. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning , 2019, Scientific Reports.
[17] Michael E. Pyle,et al. Automated clear cell renal carcinoma grade classification with prognostic significance , 2019, bioRxiv.
[18] Yun Jiang,et al. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module , 2019, PloS one.
[19] Chaoyang Zhang,et al. Deep Learning Based Analysis of Histopathological Images of Breast Cancer , 2019, Front. Genet..
[20] Thomas Walter,et al. Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map , 2019, IEEE Transactions on Medical Imaging.
[21] B. Delahunt,et al. Grading of renal cell carcinoma , 2018, Histopathology.
[22] Xiangyu Zhang,et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.
[23] In-So Kweon,et al. CBAM: Convolutional Block Attention Module , 2018, ECCV.
[24] Gang Sun,et al. Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[25] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] Anant Madabhushi,et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent , 2017, Scientific Reports.
[28] Yilong Yin,et al. Deep learning model based breast cancer histopathological image classification , 2017, 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).
[29] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[31] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] B. Delahunt,et al. The ISUP system of staging, grading and classification of renal cell neoplasia , 2014, Journal of kidney cancer and VHL.
[35] C. Kwak,et al. Application of simplified Fuhrman grading system in clear‐cell renal cell carcinoma , 2011, BJU international.
[36] Syed Furqan Qadri,et al. Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling , 2021, IEEE Access.
[37] Hieu T. Nguyen,et al. Deep Learning Applied for Histological Diagnosis of Breast Cancer , 2020, IEEE Access.
[38] In So Kweon,et al. Convolutional Block Attention Module , 2018, ECCV 2018.
[39] A. Mescher. Junqueira's Basic Histology: Text and Atlas , 2013 .