1 citations · 1 across the 5 of their papers we have counts for
9 papers
Automatic Cephalometric Landmark Localization on CBCT-Derived Digitally Reconstructed Radiographs for Skeletal Malocclusion Classification
Benjamin Hou, Konstantinia Almpani, Janice S. Lee +1
Manual cephalometric landmark annotation is important for craniofacial assessment but is labor-intensive and difficult to scale. We introduce CephViT, a Vision Transformer-based mo…
A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation
Ruida Cheng, Tejas S. Mathai, Benjamin Hou +4
In previous work, we integrated large language models (LLMs) into the lesion segmentation model based on the ULS23 DeepLesion dataset, using short-form findings from the reports. I…
ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
Joey Chan, Yikun Han, Jingyuan Chen +8
Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores differences in readers' info…
Toward Multimodal Conversational AI for Age-Related Macular Degeneration
Ran Gu, Benjamin Hou, Mélanie Hébert +5
Despite strong performance of deep learning models in retinal disease detection, most systems produce static predictions without clinical reasoning or interactive explanation. Rece…
CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography
Qingqing Zhu, Qiao Jin, Tejas S. Mathai +10
Artificial intelligence (AI) can automatically delineate lesions on computed tomography (CT) and generate radiology report content, yet progress is limited by the scarcity of publi…
Text Embedded Swin-UMamba for DeepLesion Segmentation
Ruida Cheng, Tejas Sudharshan Mathai, Pritam Mukherjee +5
Segmentation of lesions on CT enables automatic measurement for clinical assessment of chronic diseases (e.g., lymphoma). Integrating large language models (LLMs) into the lesion s…