2 citations · 4 across the 11 of their papers we have counts for
12 papers
FD-AA: A Lightweight Focal-Diffuse And Attenuation-Aware Head for Incidental Abdominal Abnormality Detection in Chest CT
Haoyan Ding, Kritika Iyer, Halid Yerebakan +9
Routine chest CT captures upper-abdominal structures that may contain clinically relevant incidental abnormalities. Detecting these findings requires feature extraction from organs…
Instance-Guided Report Anchoring for Text-Free 3D Abnormality Segmentation in Chest CT
Zhenyu Bu, Haoyan Ding, Chushu Shen +7
Accurate 3D abnormality segmentation in chest CT requires dense spatial supervision, but obtaining expert voxel-level labels is costly. Radiology reports, however, are routinely ge…
AGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classification
Peiyu Duan, Xueqi Guo, Sepehr Farhand +5
Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a report-grounded framework that…
Fast Multi-Organ Fine Segmentation in CT Images with Hierarchical Sparse Sampling and Residual Transformer
Xueqi Guo, Halid Ziya Yerebakan, Yoshihisa Shinagawa +2
Multi-organ segmentation of 3D medical images is fundamental with meaningful applications in various clinical automation pipelines. Although deep learning has achieved superior per…
BodyGPS: Anatomical Positioning System
Halid Ziya Yerebakan, Kritika Iyer, Xueqi Guo +2
We introduce a new type of foundational model for parsing human anatomy in medical images that works for different modalities. It supports supervised or unsupervised training and c…
Evaluating unsupervised contrastive learning framework for MRI sequences classification
Yuli Wang, Kritika Iyer, Sep Farhand +1
The automatic identification of Magnetic Resonance Imaging (MRI) sequences can streamline clinical workflows by reducing the time radiologists spend manually sorting and identifyin…
Real Time Multi Organ Classification on Computed Tomography Images
Halid Ziya Yerebakan, Yoshihisa Shinagawa, Gerardo Hermosillo Valadez
Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead…
A Hierarchical Descriptor Framework for On-the-Fly Anatomical Location Matching between Longitudinal Studies
Halid Ziya Yerebakan, Yoshihisa Shinagawa, Mahesh Ranganath +2
We propose a method to match anatomical locations between pairs of medical images in longitudinal comparisons. The matching is made possible by computing a descriptor of the query…
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
Yikuan Li, Hanyin Wang, Halid Yerebakan +2
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their…
Dual-Domain Cross-Iteration Squeeze-Excitation Network for Sparse Reconstruction of Brain MRI
Xiongchao Chen, Yoshihisa Shinagawa, Zhigang Peng +1
Magnetic resonance imaging (MRI) is one of the most commonly applied tests in neurology and neurosurgery. However, the utility of MRI is largely limited by its long acquisition tim…
Supervised Understanding of Word Embeddings
Halid Ziya Yerebakan, Parmeet Bhatia, Yoshihisa Shinagawa
Pre-trained word embeddings are widely used for transfer learning in natural language processing. The embeddings are continuous and distributed representations of the words that pr…
Hierarchical Latent Word Clustering
Halid Ziya Yerebakan, Fitsum Reda, Yiqiang Zhan +1
This paper presents a new Bayesian non-parametric model by extending the usage of Hierarchical Dirichlet Allocation to extract tree structured word clusters from text data. The inf…