# AI in cancer detection

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/ai-in-cancer-detection-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 956,688 |
| Description | This cluster of papers focuses on the application of deep learning and machine learning techniques in medical image analysis, particularly in the context of histopathology images, digital pathology, and computer-aided detection for breast cancer diagnosis. The use of convolutional neural networks and whole slide imaging is prominent in these studies, aiming to improve accuracy and efficiency in cancer prognosis and prediction. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T10862 |
| Works | 85,027 |

## Topic researchers

Showing 12 of 20.

- [David Moher](https://scholariq.org/researchers/david-moher/)
- [Kaiming He](https://scholariq.org/researchers/kaiming-he/)
- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [Robert Tibshirani](https://scholariq.org/researchers/robert-tibshirani/)
- [Gad Getz](https://scholariq.org/researchers/gad-getz/)
- [Ross Girshick](https://scholariq.org/researchers/ross-girshick/)
- [Elaine R. Mardis](https://scholariq.org/researchers/elaine-r-mardis/)
- [Hermann Brenner](https://scholariq.org/researchers/hermann-brenner/)
- [Matthias Egger](https://scholariq.org/researchers/matthias-egger/)
- [John P. A. Ioannidis](https://scholariq.org/researchers/john-p-a-ioannidis/)
- [Karl Friston](https://scholariq.org/researchers/karl-friston/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.