# AI in cancer detection

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

## Facts

| Field | Value |
| --- | --- |
| 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 | t10862 |
| Works | 487 |

## Topic papers all

Showing 15 of 487.

- [The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)](https://scholariq.org/papers/the-multimodal-brain-tumor-image-segmentation-benchmark-brats/)
- [Radiomics: Extracting more information from medical images using advanced feature analysis](https://scholariq.org/papers/radiomics-extracting-more-information-from-medical-images-using-advanced-feature/)
- [International evaluation of an AI system for breast cancer screening](https://scholariq.org/papers/international-evaluation-of-an-ai-system-for-breast-cancer-screening/)
- [Clinical-grade computational pathology using weakly supervised deep learning on whole slide images](https://scholariq.org/papers/clinical-grade-computational-pathology-using-weakly-supervised-deep-learning-on/)
- [Deep Learning for Health Informatics](https://scholariq.org/papers/deep-learning-for-health-informatics/)
- [The molecular portraits of breast tumors are conserved across microarray platforms](https://scholariq.org/papers/the-molecular-portraits-of-breast-tumors-are-conserved-across-microarray/)
- [Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network](https://scholariq.org/papers/lung-pattern-classification-for-interstitial-lung-diseases-using-a-deep/)
- [Deep learning-enabled medical computer vision](https://scholariq.org/papers/deep-learning-enabled-medical-computer-vision/)
- [Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images](https://scholariq.org/papers/exploring-the-effect-of-image-enhancement-techniques-on-covid-19-detection-using/)
- [Human–computer collaboration for skin cancer recognition](https://scholariq.org/papers/human-computer-collaboration-for-skin-cancer-recognition/)
- [Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis](https://scholariq.org/papers/diagnostic-accuracy-of-deep-learning-in-medical-imaging-a-systematic-review-and/)
- [Repeatability and Reproducibility of Radiomic Features: A Systematic Review](https://scholariq.org/papers/repeatability-and-reproducibility-of-radiomic-features-a-systematic-review/)
- [The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions](https://scholariq.org/papers/the-uk-biobank-imaging-enhancement-of-100-000-participants-rationale-data/)
- [Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study](https://scholariq.org/papers/real-time-automatic-detection-system-increases-colonoscopic-polyp-and-adenoma/)
- [Detection of Coronavirus Disease (COVID-19) Based on Deep Features](https://scholariq.org/papers/detection-of-coronavirus-disease-covid-19-based-on-deep-features/)

## Topic primary papers

Showing 15 of 184.

- [International evaluation of an AI system for breast cancer screening](https://scholariq.org/papers/international-evaluation-of-an-ai-system-for-breast-cancer-screening/)
- [Clinical-grade computational pathology using weakly supervised deep learning on whole slide images](https://scholariq.org/papers/clinical-grade-computational-pathology-using-weakly-supervised-deep-learning-on/)
- [Transformer-based unsupervised contrastive learning for histopathological image classification](https://scholariq.org/papers/transformer-based-unsupervised-contrastive-learning-for-histopathological-image/)
- [Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists](https://scholariq.org/papers/stand-alone-artificial-intelligence-for-breast-cancer-detection-in-mammography/)
- [National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium](https://scholariq.org/papers/national-performance-benchmarks-for-modern-screening-digital-mammography-update/)
- [Influence of Computer-Aided Detection on Performance of Screening Mammography](https://scholariq.org/papers/influence-of-computer-aided-detection-on-performance-of-screening-mammography/)
- [Towards a guideline for evaluation metrics in medical image segmentation](https://scholariq.org/papers/towards-a-guideline-for-evaluation-metrics-in-medical-image-segmentation/)
- [Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study](https://scholariq.org/papers/artificial-intelligence-supported-screen-reading-versus-standard-double-reading/)
- [A pathology foundation model for cancer diagnosis and prognosis prediction](https://scholariq.org/papers/a-pathology-foundation-model-for-cancer-diagnosis-and-prognosis-prediction/)
- [Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes](https://scholariq.org/papers/refining-the-accuracy-of-validated-target-identification-through-coding-variant/)
- [Assessment of algorithms for mitosis detection in breast cancer histopathology images](https://scholariq.org/papers/assessment-of-algorithms-for-mitosis-detection-in-breast-cancer-histopathology/)
- [A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework](https://scholariq.org/papers/a-machine-learning-approach-to-diagnosing-lung-and-colon-cancer-using-a-deep/)
- [A support vector machine-based ensemble algorithm for breast cancer diagnosis](https://scholariq.org/papers/a-support-vector-machine-based-ensemble-algorithm-for-breast-cancer-diagnosis/)
- [Self-supervised learning for medical image classification: a systematic review and implementation guidelines](https://scholariq.org/papers/self-supervised-learning-for-medical-image-classification-a-systematic-review/)
- [Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.](https://scholariq.org/papers/robust-breast-cancer-detection-in-mammography-and-digital-breast-tomosynthesis/)

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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.