11 citations · 12 across the 5 of their papers we have counts for
5 papers
A Controlled Evaluation of Quantum Correlation Refinement for Few-Shot Semantic Segmentation: Resource Cost and IBM Quantum Hardware Validation
Hina Shakir, Muhammad Irfan Memon, Asia Samreen +4
PQCs are increasingly proposed as trainable components in classical ML pipelines, but rarely characterized alongside a controlled measurement of task-level benefit. We report such…
Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection
Hina Shakir, Mohammad Mohatram, Javeed Hussain +2
Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics dataset…
Benchmarking Vanilla GAN, DCGAN, and WGAN Architectures for MRI Reconstruction: A Quantitative Analysis
Humaira Mehwish, Hina Shakir, Muneeba Rashid +2
Magnetic Resonance Imaging (MRI) is a crucial imaging modality for viewing internal body structures. This research work analyses the performance of popular GAN models for accurate…
Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM
Majid Ali, Hina Shakir, Asia Samreen +1
Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accu…
Radiomic feature selection for lung cancer classifiers
Hina Shakir, Haroon Rasheed, Tariq Mairaj Rasool Khan
Machine learning methods with quantitative imaging features integration have recently gained a lot of attention for lung nodule classification. However, there is a dearth of studie…