6 citations · 11 across the 8 of their papers we have counts for
8 papers
AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection
Md Mahfuzur Rahman Siddiquee, Fazle Rafsani, Jay Shah +4
Abnormality detection is a crucial yet challenging task in medical image analysis. Distinguishing abnormalities from normal data by learning to reconstruct normal-only data allevia…
Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
Alejandro Lozano, Keiko Ihara, Ping-Hao Yang +13
Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care. Yet clinicians face increasing c…
PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics
Kihyuk Yoon, Lingchao Mao, Catherine Chong +3
Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence models can be costly and data-hun…
MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series
Dohyun Ku, Catherine D. Chong, Visar Berisha +2
Time series analysis has emerged as an important tool for improving patient diagnosis and management in healthcare applications. However, these applications commonly face two criti…
DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Fazle Rafsani, Jay Shah, Catherine D. Chong +2
Anomaly detection and classification in medical imaging are critical for early diagnosis but remain challenging due to limited annotated data, class imbalance, and the high cost of…
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images
Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu +5
Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare disease…
Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media
Yuting Guo, Swati Rajwal, Sahithi Lakamana +10
Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health informatio…
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu +3
Automated anomaly detection from medical images, such as MRIs and X-rays, can significantly reduce human effort in disease diagnosis. Owing to the complexity of modeling anomalies…