1 code implementation • 22 Apr 2024 • Mohammad Areeb Qazi, Ibrahim Almakky, Anees Ur Rehman Hashmi, Santosh Sanjeev, Mohammad Yaqub
DynaMMo achieves this without compromising performance, offering a cost-effective solution for continual learning in medical applications.
no code implementations • 27 Mar 2024 • Anees Ur Rehman Hashmi, Dwarikanath Mahapatra, Mohammad Yaqub
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging.
no code implementations • 20 Mar 2024 • Santosh Sanjeev, Nuren Zhaksylyk, Ibrahim Almakky, Anees Ur Rehman Hashmi, Mohammad Areeb Qazi, Mohammad Yaqub
The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP.
no code implementations • 18 Mar 2024 • Ibrahim Almakky, Santosh Sanjeev, Anees Ur Rehman Hashmi, Mohammad Areeb Qazi, Mohammad Yaqub
In this work, we propose MedMerge, a method whereby the weights of different models can be merged, and their features can be effectively utilized to boost performance on a new task.
no code implementations • 14 Mar 2024 • Fadillah Maani, Anees Ur Rehman Hashmi, Mariam Aljuboory, Numan Saeed, Ikboljon Sobirov, Mohammad Yaqub
This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.
1 code implementation • 14 Mar 2024 • Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi, Santosh Sanjeev, Vijay Ram Papineni, Dwarikanath Mahapatra, Mohammad Yaqub
Large-scale generative models have demonstrated impressive capacity in producing visually compelling images, with increasing applications in medical imaging.