Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

By Babak Ehteshami Bejnordi, M. Veta, Paul Johannes van Diest, B. van Ginneken, Nico Karssemeijer, Geert J. S. Litjens, Jeroen A. W. M. van der Laak, M. Hermsen, Quirine F. Manson, M. Balkenhol and 60 more. 2017

3,174 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cancer Research, Pathology and Forensic Medicine, Medical Imaging, Medical & Health AI

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Citations in the corpus

Group Equivariant Stand-Alone Self-Attention For Vision (2020, methodology): “The PatchCamelyon dataset (Veeling et al., 2018) consists of 327 k 96x96 RGB image patches of tumorous/non-tumorous breast tissues extracted from Camelyon16 (Bejnordi et al., 2017).”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “However, this practice has not been ubiquitous due to the difficulty of obtaining data spanning multiple hospitals (Esteva et al., 2017; Bejnordi et al., 2017; Codella et al., 2019; Veta et al., 2019).”
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology (2019, methodology): “This popular method has been used by numerous researchers in recent public chal-6 lenges (B´andi et al. (2019); Bejnordi et al. (2017)).”
Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology (2019, methodology): “Many methods have been proposed for pattern recognition tasks in pathology: Patient-level and WSI-level pN-stage estimation (CAMELYON 2016 and 2017) [4][5], and segmentation of tumors.”
Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline (2018, background): “Convolutional neural networks (CNNs) have achieved expert-level performance in complex visual recognition tasks, including the diagnosis of skin 27 and breast 28,29 cancers.”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, methodology): “org restricted to 40× magnification only), or (2) they retrained their model MSINet on the same training set as used by Kather and colleagues and applied it to their internal dataset (n = 100): here they report an AUC of 0.”
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018, background): “The authors thank the Open Data Science community [27] for useful suggestions and other help aiding the development of this work.”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, neutral): “ut (Fashion-)MNIST, CIFAR-10, STL-10, ImageNet Synthetic MNIST-C [413], ImageNet-C [414], ImageNet-P [414], ImageNet-O [419] Real-world Industrial: MVTec-AD [189], PCB [420] Medical: CAMELYON16 [61], [421], NIH Chest Xray [61], [422], MOOD [423], HCP/BRATS [52], Neuropathology [60], [424] Security: ”
Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov (2020, background): “, pathological diagnosis (8) and endoscopic diagnosis (9)], the performance of AI is as good as that of human experts.”
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images (2021, background): “…occupies a tiny proportion of the total image ( i.e. - a needle-in-a-haystack problem such as differentiating normal tissue vs micro-metastases) [4, 3, 5, 45], or 2) spanning the entire composition of a WSI and dependent on the interactions of its components ( i.e. - a ﬁne-grained visual…”