5 papers · 1 filter
T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis
Raza Imam, Hu Wang, Dwarikanath Mahapatra +1
In medical imaging, vision-language models face a critical duality: pretrained networks offer broad robustness but lack subtle, modality-specific characteristics, while fine-tuned…
DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis
Numan Saeed, Tausifa Jan Saleem, Fadillah Maani +3
Deep learning for medical imaging is hampered by task-specific models that lack generalizability and prognostic capabilities, while existing 'universal' approaches suffer from simp…
Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach
Daniil Tikhonov, Matheus Scatolin, Mohor Banerjee +7
Accurate evaluation of the response of glioblastoma to therapy is crucial for clinical decision-making and patient management. The Response Assessment in Neuro-Oncology (RANO) crit…
Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Dongli He, Hu Wang, Mohammad Yaqub
Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements…
In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Models
Hu Wang, Ibrahim Almakky, Congbo Ma +2
Model merging is an effective strategy to merge multiple models for enhancing model performances, and more efficient than ensemble learning as it will not introduce extra computati…