Publications Syed Rakin Ahmed
Papers
Reproducible and clinically translatable deep neural networks for cervical screening.
Power spectral analysis can determine language laterality from resting-state functional MRI data in healthy controls.
Radiotherapy-induced Cherenkov luminescence imaging in a human body phantom.
Assessing generalizability of an AI-based visual test for cervical cancer screening.
Generalizable deep neural networks for image quality classification of cervical images.
Estimating test performance for AI medical devices under distribution shift with conformal prediction.
Frequency-Domain Resting State fMRI Analysis Demonstrates Language Lateralization in Healthy Controls.
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data.
Artificial intelligence applied to musculoskeletal oncology: a systematic review.
Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.
Opportunities and Challenges for Deep Learning in Brain Lesions.
Systemic coagulation is activated in patients with meningioma and glioblastoma.
An assessment of the feasibility of local oxygen sensing with radiotherapy induced luminescence from Cherenkov sheet imaging.
Abnormal vascular structure and function within brain metastases is linked to pembrolizumab resistance.
Use of risk-based cervical screening programs in resource-limited settings.
AI-based image analysis in clinical testing: lessons from cervical cancer screening.
Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy.
Coagulation Activation in Brain Neoplasms
Improving the repeatability of deep learning models using novel loss function combination and optimization approaches.
Design and multi-rater evaluation of a cervical visual treatability classifier.
CervSSL: foundation models for cervical cancer screening using self-supervised learning.
An assessment of novel distance metric correlation with out-of-distribution performance of deep learning models.
A robust, tiered deep neural network for detection of actionable genomic alterations in brain metastases.
Automatic deep learning-based segmentation and treatment response assessment of brain metastases on magnetic resonance imaging.
AdrenoBERT: automated multiclass categorization of adrenal abnormalities from free-text radiology reports using transformer language models.
Federated learning for prostate cancer vendor neutral models.
Addressing catastrophic forgetting for medical domain expansion.
A diffusion-inspired self-supervised framework for affine medical image registration.
The process of disentangling AI features associates with race and ethnicity in retinal images.
Evaluation of a novel contrast mechanism utilizing nanoparticles as contrast agents for electrical property imaging of the prostate and breast
Presentations and Symposia
A deep learning framework enables non-invasive detection of tumor mutational burden in brain metastases.
A deep learning-based framework for joint image registration and segmentation of brain metastases on magnetic resonance imaging.
Focal loss improves clinical deployability of deep learning models.
Estimating test performance for AI medical devices under distribution shift with conformal prediction.
Focal loss improves repeatability of deep learning models.
Frequency-Domain Resting State fMRI Analysis to Investigate Language and Handmotor Cortices in Healthy Controls.
Frequency-Domain Resting State fMRI Analysis to Investigate Language and Handmotor Cortices in Healthy Controls.
Deep learning-based non-invasive molecular profiling of brain metastases from MR imaging.
Implications for squamocolumnar junction visibility on the performance of Automated Visual Evaluation algorithm for cervical cancer screening
Deep learning-based cervical cancer visual evaluation test: designing the test result as 2-class or 3-class.
Conformal prediction and Monte Carlo inference for addressing uncertainty in cervical cancer screening.
Deep learning-based prediction of breast cancer tumor and immune phenotypes from digitized histopathology.
A deep learning algorithm for fully automated volumetric measurement of meningioma burden.
Coagulation Activation in Brain Neoplasms.
Neurovascular Coupling in Normal Brain and in Disease: A Multimodal Imaging Approach.
Hamiltonian Dynamics of Love Triangles.
Self-supervision and diabetic retinopathy.
Unravelling the effectiveness of AI-based race prediction model.
Monitoring disease progression with stable diffusion: a visual approach.