Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
By Zizhao Zhang, Pingjun Chen, Mason McGough, Fuyong Xing, Chunbao Wang, M. Bui, Yuanpu Xie, Manish Sapkota, Lei Cui, J. Dhillon and 8 more. 2019
318 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wojciech Samek
- K. Müller
- Grgoire Montavon
- Sebastian Lapuschkin
- Christopher J. Anders
- Alexander Binder
- Longo Luca
- Giulia Vilone
In Radiology, Nuclear Medicine and Imaging
- Grgoire Montavon
- Wojciech Samek
- K. Müller
- Francesco Ciompi
- J. A. van der Laak
- Ozan Ciga
- Geert J. S. Litjens
- Anne L. Martel
Citations in the corpus
- From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, unknown): “created an ML model that learns to generateexplanations innatural language for itspredictionsduring training.(127)Posthocmethodsare convenient because theycanbe applied to most models without requiring specialized training, but models with interpretability built in may provide more reliable explanat”
- Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “[204] also generate a free-text pathology report to clarify the decision of the classifier.”
- Deep learning in histopathology: the path to the clinic (2021, methodology): “As the scale of data-sets grew, CPATH methods began to approach and even surpass the performance of pathologists 4,5,76 .”
- Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, background): “In addition to visual explanations, [161] also generate a free-text pathology report to clarify the decision of the classifier.”
- Deep neural network models for computational histopathology: A survey (2019, background): “, 2014; Ertosun and Rubin, 2015) to more sophisticated models (Qaiser and Rajpoot, 2019; Zhang et al., 2019) for accurate tissue-level cancer localization and WSI-level disease grading.”
- Medical Image Segmentation Using Deep Learning: A Survey (2020, background): “Therefore, the research on the interpretability of deep learning for medical image segmentation [145] [146] [147] [148] will be a popular direction in future.”