Jungkyu Park

TL;DR: In this paper, a two-stage architecture and training procedure was proposed for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 100,000 images).

TL;DR: A deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams, and it is shown that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of the neural network is more accurate than either of the two separately.

TL;DR: This work proposes a novel neural network model that is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings in screening mammography interpretation: predicting the presence or absence of benign and malignant lesions.

TL;DR: In this article, the authors proposed a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model.

TL;DR: In this paper, an artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images has been presented, which is an important imaging modality for the detection and characterization of breast cancer.