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Tanjida Kabir on Muck Rack

Tanjida Kabir

As seen in: Texas Medical Center
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Articles

Deep Learning Frameworks for Disease Diagnosis and Overall Survival Prediction using Imaging, Genomic and Clinical Data

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By Tanjida Kabir
| Texas Medical Center
Dissertations and Theses (Open Access) Graduation Date Fall 2025 Degree Name Doctor of Philosophy (PhD) School Name McWilliams School of Biomedical Informatics at UTHealth Houston Advisory Committee Xiaoqian Jiang, PhD Abstract Deep learning has transformed multiple domains, yet clinical data remain challenging due to severe class imbalance, missing values, heterogeneous multimodal inputs, and distribution shifts across scanners and institutions.
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Use of the Deep Learning Approach to Measure Alveolar Bone Level

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By Chun-Teh Lee, Tanjida Kabir, Sally Sheng, Jiman Nelson
| Texas Medical Center
AIM: The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis. MATERIALS AND METHODS: A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth.
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"A Comprehensive Artificial Intelligence Framework for Dental Diagnosis" by Tanjida Kabir, Chun-Teh Lee et al.

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By Tanjida Kabir, Chun-Teh Lee, Luyao Chen, Xiaoqian Jiang
| Texas Medical Center
BACKGROUND: The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses.
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