5 papers
U-DFA: A Unified DINOv2-Unet with Dual Fusion Attention for Multi-Dataset Medical Segmentation
Zulkaif Sajjad, Furqan Shaukat, Junaid Mir
Accurate medical image segmentation plays a crucial role in overall diagnosis and is one of the most essential tasks in the diagnostic pipeline. CNN-based models, despite their ext…
EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images
Hafza Eman, Furqan Shaukat, Muhammad Hamza Zafar +1
Objective: Lung cancer is a leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and poor early detection. This study aims to develop a computer-…
Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT Images
Muniba Noreen, Furqan Shaukat
Lung cancer remains among the deadliest types of cancer in recent decades, and early lung nodule detection is crucial for improving patient outcomes. The limited availability of an…
Attention-ResUNet and EfficientSASM-UNet: UNet based frameworks for Lung and Nodule segmentation
Muhammad Abdullah, Furqan Shaukat
Lung cancer has been one of the major threats across the world with the highest mortalities. Computer-aided detection (CAD) can help in early detection and thus can help increase t…
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images
Furqan Shaukat, Syed Muhammad Anwar, Abhijeet Parida +3
Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characte…