565 citations · 1.8k across the 38 of their papers we have counts for
6 papers · 1 filter
Do Attention Heads in BERT Track Syntactic Dependencies?
Phu Mon Htut, Jason Phang, Shikha Bordia +1
We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations…
Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs
Alex Warstadt, Yu Cao, Ioana Grosu +13
Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical k…
Improving localization-based approaches for breast cancer screening exam classification
Thibault Févry, Jason Phang, Nan Wu +4
We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200,000 exams (over 1,000,000 images). Our model achieves an AUC of 0…
Screening Mammogram Classification with Prior Exams
Jungkyu Park, Jason Phang, Yiqiu Shen +5
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new ne…
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Yiqiu Shen, Nan Wu, Jason Phang +5
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural…
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Nan Wu, Jason Phang, Jungkyu Park +29
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network ach…