A deep semantic mobile application for thyroid cytopathology
By Edward Kim, M. Côrte-Real, Zubair Baloch. 2016
87 citations recorded by the source corpus.
Cytopathology is the study of disease at the cellular level and often used as a screening tool for cancer. Thyroid cytopathology is a branch of pathology that studies the diagnosis of thyroid lesions and diseases. A pathologist views cell images that may have high visual variance due to different anatomical structures and pathological characteristics. To assist the physician with identifying and searching through images, we propose a deep semantic mobile application. Our work augments recent advances in the digitization of pathology and machine learning techniques, where there are transformative opportunities for computers to assist pathologists. Our system uses a custom thyroid ontology that can be augmented with multimedia metadata extracted from images using deep machine learning techniques. We describe the utilization of a particular methodology, deep convolutional neural networks, to the application of cytopathology classification. Our method is able to leverage networks that have been trained on millions of generic images, to medical scenarios where only hundreds or thousands of images exist. We demonstrate the benefits of our framework through both quantitative and qualitative results.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology
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
- Francesco Ciompi
- J. A. van der Laak
- Geert J. S. Litjens
- Ala Al-Fuqaha
- Junaid Qadir
- Muhammad Bilal
- Clara I. Sánchez
- Paul Patras
In Computer Vision and Pattern Recognition
- Geert J. S. Litjens
- J. A. van der Laak
- Muhammad Bilal
- Ala Al-Fuqaha
- Francesco Ciompi
- Clara I. Sánchez
- Paul Patras
- Pegah Khosravi
In Molecular Biology
- Muhammad Bilal
- Ala Al-Fuqaha
- Paul Patras
- Olivier Elemento
- Junaid Qadir
- Hamed Haddadi
- Chaoyun Zhang
- B. van Ginneken
Citations in the corpus
- A survey on deep learning in medical image analysis (2017, methodology): “The two papers that do, Antony et al. (2016) and Kim et al. (2016a), o ff er con-flicting results.”
- Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images (2017, methodology): “While, Kim et al. (2016) showed that using pre-trained network as a feature extractor slightly outperformed fine-tuning in cytopathology image classification (70.5% versus 69.1%).”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[210] Cytopathology classification Cloud-based CNN Sathyanarayana et al.”
- Comparison of Deep Transfer Learning Strategies for Digital Pathology (2018, unknown): “Custom [10, 16] AlexNet (and variants) [14, 2, 1, 13, 15, 11, 8] GoogLeNet (InceptionV1) [14, 2] VGG16 [7, 2, 1, 16, 6, 11] InceptionV3 [7, 4, 5] ResNet50 (and variants) [16]”
- Secure and Robust Machine Learning for Healthcare: A Survey (2020, result): “For instance, results obtained by transfer learning in [48] and [49] are contradictory.”