Výsledky vyhledávání - "Chetty, Indrin"

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Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction

Autor: Khanmohammadi, Reza, Ghanem, Ahmed I., Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Luo, Bing, Chetty, Indrin J., Alhanai, Tuka, Thind, Kundan, Ghassemi, Mohammad M.

Large Language Models (LLMs) offer significant potential for clinical symptom extraction, but their deployment in healthcare settings is constrained by privacy concerns, computational limitations, and operational costs. This study investigates the op

Externí odkaz: http://arxiv.org/abs/2408.04775

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Iterative Prompt Refinement for Radiation Oncology Symptom Extraction Using Teacher-Student Large Language Models

Autor: Khanmohammadi, Reza, Ghanem, Ahmed I, Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Chetty, Indrin, Ghassemi, Mohammad M., Thind, Kundan

This study introduces a novel teacher-student architecture utilizing Large Language Models (LLMs) to improve prostate cancer radiotherapy symptom extraction from clinical notes. Mixtral, the student model, initially extracts symptoms, followed by GPT

Externí odkaz: http://arxiv.org/abs/2402.04075

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An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology

Autor: Khanmohammadi, Reza, Ghassemi, Mohammad M., Verdecchia, Kyle, Ghanem, Ahmed I., Bing, Luo, Chetty, Indrin J., Bagher-Ebadian, Hassan, Siddiqui, Farzan, Elshaikh, Mohamed, Movsas, Benjamin, Thind, Kundan

Natural Language Processing (NLP) is a key technique for developing Medical Artificial Intelligence (AI) systems that leverage Electronic Health Record (EHR) data to build diagnostic and prognostic models. NLP enables the conversion of unstructured c

Externí odkaz: http://arxiv.org/abs/2311.02205

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AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation

Autor: Li, Chengyin, Khanduri, Prashant, Qiang, Yao, Sultan, Rafi Ibn, Chetty, Indrin, Zhu, Dongxiao

Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. However, in clinical settings, creating effective prompts is notably ch

Externí odkaz: http://arxiv.org/abs/2308.14936

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FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images

Autor: Li, Chengyin, Qiang, Yao, Sultan, Rafi Ibn, Bagher-Ebadian, Hassan, Khanduri, Prashant, Chetty, Indrin J., Zhu, Dongxiao

Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-base

Externí odkaz: http://arxiv.org/abs/2210.03189

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Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization

Autor: Zong, Weiwei, Carver, Eric, Zhu, Simeng, Schaff, Eric, Chapman, Daniel, Lee, Joon, Ebadian, Hassan Bagher, Chetty, Indrin, Movsas, Benjamin, Wen, Winston, Alafif, Tarik, Zong, Xiangyun

Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work whe

Externí odkaz: http://arxiv.org/abs/2206.06235

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