41 citations · 64 across the 22 of their papers we have counts for
27 papers
SARFA: Segment Anything with Radiomic Feature Alignment
Tyler Ward, Abdullah Imran
The Segment Anything Model (SAM) has demonstrated strong generalizability across a variety of segmentation tasks. However, SAM often struggles in situations where the target to be…
Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?
Nusrat Munia, Tyler Ward, Nishat Nayla +2
Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approach for self-supervised learn…
Protecting and Preserving Protest Dynamics for Responsible Analysis
Cohen Archbold, Usman Hassan, Nazmus Sakib +2
Protest-related social media data are valuable for understanding collective action but inherently high-risk due to concerns surrounding surveillance, repression, and individual pri…
CAP-IQA: Context-Aware Prompt-Guided CT Image Quality Assessment
Kazi Ramisa Rifa, Jie Zhang, Abdullah Imran
Prompt-based methods, which encode medical priors through descriptive text, have been only minimally explored for CT Image Quality Assessment (IQA). While such prompts can embed pr…
ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis
Tyler Ward, Abdullah Imran
Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation. However, issues relating to se…
Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
Nusrat Munia, Abdullah Imran
Generative models, especially Diffusion Models, have demonstrated remarkable capability in generating high-quality synthetic data, including medical images. However, traditional cl…
A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation
Tyler Ward, Abdullah Imran
Recent advances in promptable segmentation, such as the Segment Anything Model (SAM), have enabled flexible, high-quality mask generation across a wide range of visual domains. How…
Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive Learning
Tyler Ward, Xiaoqin Wang, Braxton McFarland +6
Complete removal of cancer tumors with a negative specimen margin during lumpectomy is essential in reducing breast cancer recurrence. However, 2D specimen radiography (SR), the cu…
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation
Tyler Ward, Aaron Moseley, Abdullah-Al-Zubaer Imran
Segmentation is one of the most important tasks in the medical imaging pipeline as it influences a number of image-based decisions. To be effective, fully supervised segmentation a…
Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Images
Nusrat Munia, Abdullah-Al-Zubaer Imran
Artificial Intelligence (AI) in skin disease diagnosis has improved significantly, but a major concern is that these models frequently show biased performance across subgroups, esp…
Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks
Tyler Ward, Abdullah-Al-Zubaer Imran
Quantifying functional connectivity (FC), a vital metric for the diagnosis of various brain disorders, traditionally relies on the use of a pre-defined brain atlas. However, using…
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis
Nusrat Munia, Abdullah-Al-Zubaer Imran
Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the…
EarthScape: A Multimodal Benchmark for Geologic Mapping and Surface-Aware Learning under Geographic Domain Shift
Matthew Massey, Matthew A. Massey, Nusrat Munia +1
Understanding natural Earth surface processes requires integrating topography, imagery, hydrology, and human infrastructure while remaining robust to geographic variation, yet few…
Scout-Net: Prospective Personalized Estimation of CT Organ Doses from Scout Views
Abdullah-Al-Zubaer Imran, Sen Wang, Debashish Pal +4
Purpose: Estimation of patient-specific organ doses is required for more comprehensive dose metrics, such as effective dose. Currently, available methods are performed retrospectiv…
Forward-Forward Contrastive Learning
Md. Atik Ahamed, Jin Chen, Abdullah-Al-Zubaer Imran
Medical image classification is one of the most important tasks for computer-aided diagnosis. Deep learning models, particularly convolutional neural networks, have been successful…
Semi-Supervised Relational Contrastive Learning
Attiano Purpura-Pontoniere, Demetri Terzopoulos, Adam Wang +1
Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-superv…
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder
Yeahia Sarker, Abdullah-Al-Zubaer Imran, Md Hafiz Ahamed +3
Deep learning-based super-resolution methods have shown great promise, especially for single image super-resolution (SISR) tasks. Despite the performance gain, these methods are li…
Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data
Ayaan Haque, Abdullah-Al-Zubaer Imran, Adam Wang +1
Deep learning-based models, when trained in a fully-supervised manner, can be effective in performing complex image analysis tasks, although contingent upon the availability of lar…
Window-Level is a Strong Denoising Surrogate
Ayaan Haque, Adam Wang, Abdullah-Al-Zubaer Imran
CT image quality is heavily reliant on radiation dose, which causes a trade-off between radiation dose and image quality that affects the subsequent image-based diagnostic performa…
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
Ayaan Haque, Abdullah-Al-Zubaer Imran, Adam Wang +1
Semi-supervised learning via learning from limited quantities of labeled data has been investigated as an alternative to supervised counterparts. Maximizing knowledge gains from co…
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conv…
Progressive Adversarial Semantic Segmentation
Abdullah-Al-Zubaer Imran, Demetri Terzopoulos
Medical image computing has advanced rapidly with the advent of deep learning techniques such as convolutional neural networks. Deep convolutional neural networks can perform excee…
Partly Supervised Multitask Learning
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model…
Analysis of Scoliosis From Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Spi…