Search Results for author:
Found 7 papers, 1 papers with code
Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
For framework evaluation, we assembled two datasets retrospectively.
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection
The model also incorporates a knowledge-based loss weighting technique, which assigns different weights to the loss function at the class level and the instance level, helping the model focus on minority classes and challenging samples.
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images
We conducted a thorough investigation of the Segment Anything Model (SAM) for the task of interactive segmentation of breast tumors in ultrasound images.
Polyp-SAM: Transfer SAM for Polyp Segmentation
In this study, we propose Poly-SAM, a finetuned SAM model for polyp segmentation, and compare its performance to several state-of-the-art polyp segmentation models.
SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model
Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment.
Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT
In this paper, we aimed to provide a review and tutorial for researchers in the field of medical imaging using language models to improve their tasks at hand.
Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions
Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce.
