306 citations · 349 across the 6 of their papers we have counts for
7 papers
ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT
Huseyin Umut Isik, Mehmet Alp Ozaydin, Sila Kurugol +1
Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated labels. However, two characteris…
WristMIR: Coarse-to-Fine Region-Aware Retrieval of Pediatric Wrist Radiographs with Radiology Report-Driven Learning
Mert Sonmezer, Serge Vasylechko, Duygu Atasoy +2
Retrieving wrist radiographs with analogous fracture patterns is challenging because clinically important cues are subtle, highly localized and often obscured by overlapping anatom…
TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection
Abdulkarim Atrash, Omar Moured, Yufan Chen +3
Infrared small target detection (IRSTD) is critical for defense and surveillance but remains challenging due to (1) target loss from minimal features, (2) false alarms in cluttered…
CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationarity
Mert Sonmezer, Seyda Ertekin
Long-term time series forecasting plays a pivotal role in various real-world applications. Despite recent advancements and the success of different architectures, forecasting is of…
CALPAGAN: Calorimetry for Particles using GANs
Ebru Simsek, Bora Isildak, Anil Dogru +3
In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Gener…
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Ümit Çavuş Büyükşahin, Şeyda Ertekin
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting m…
Learning to Predict the Wisdom of Crowds
Seyda Ertekin, Haym Hirsh, Cynthia Rudin
The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently…