Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

By Angel Cruz-Roa, Ajay Basavanhally, Fabio A. González, Hannah Gilmore, M. Feldman, Shridar Ganesan, Natalie N. C. Shih, John Tomaszewski, Anant Madabhushi. 2014

632 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology, Medical Imaging

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Anant Madabhushi

In Medical Imaging

Citations in the corpus

QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019, background): “Five of these are privacy sensitive in nature, as they include health datasets—Thyroid [45], Breast cancer [8], MotionSense [38], Skin cancer MNIST [53] and a financial credit dataset—German credit [12].”
A survey on deep learning in medical image analysis (2017, neutral): “Cruz-Roa et al. (2014) Detection of invasive ductal carcinoma H&E CNN-based patch classifier”
Detecting Cancer Metastases on Gigapixel Pathology Images (2017, background): “[7] demonstrated that CNNs achieved higher F1 score and balanced accuracy in detecting invasive ductal carcinoma.”
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning (2020, background): “Training a CNN for good feature representations in MIL is non-trivial for WSI analysis, due to the prohibitive memory requirement and the noisy supervisory signal [28].”
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification (2015, methodology): “To predict the image-level label, max-pooling (SMI) and voting (average-pooling) were applied in [36, 30, 17].”
Deep learning in histopathology: the path to the clinic (2021, methodology): “Methods that used entire WSIs followed for applications such as breast cancer segmentation 17 , glioma classification 18 , non-alcoholic fatty liver disease 19 , assessment of renal transplant biopsies 20 and prostate cancer detection 21 .”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, neutral): “Deep learning techniques have been successfully applied in many problems such as arrhythmia detection [28] [29] [30] , skin cancer classification [31, 32] , breast cancer detection [33, 34] , brain disease classification [35] , pneumonia detection from chest X-ray images [36] , fundus image segmenta”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “Medical images analysis CNN Brain tumor segmentation, won top 2 in BRATS [108] Segmentation of pancreas in CT [113] Knee cartilage segmentation [114] Segmentation of hippocampus [117] Predict semantic descriptions from medical images [118] Segmentation of MR brain images [121] Anatomy-specific class”
Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology (2019, methodology): “Patch-based approach: The patch-based methods segment the large WSI into small image patches and then classify each patch image [2][6][13][33][41][49].”
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, methodology): “In particular, detection of invasive ductal carcinomas (IDCs), [9] mitosis detection, [8] neuron segmentation, [15] colon gland segmentation, [16] nuclei segmentation [17-19] and detection, [20] brain tumor classification, [21] epithelial tumor nuclei identification, [22] epithelium segmentation,…”