6 papers
Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation
Amirhossein Movahedisefat, Amirreza Fateh, Mohammad Reza Mohammadi
Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundation models like the Segment An…
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification
Amirreza Fateh, Yasin Rezvani, Sara Moayedi +4
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, primarily due to the lack of high-qu…
MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping
Amirreza Fateh, Mohammad Reza Mohammadi, Mohammad Reza Jahed Motlagh
Few-shot Semantic Segmentation addresses the challenge of segmenting objects in query images with only a handful of annotated examples. However, many previous state-of-the-art meth…
Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach
Alireza Saber, Amirreza Fateh, Pouria Parhami +3
Pneumonia, a prevalent respiratory infection, remains a leading cause of morbidity and mortality worldwide, particularly among vulnerable populations. Chest X-rays serve as a prima…
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms
Fatemeh Askari, Amirreza Fateh, Mohammad Reza Mohammadi
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metr…
FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation
Sadjad Rezvani, Mansoor Fateh, Yeganeh Jalali +1
Early detection of lung cancer is crucial as it increases the chances of successful treatment. Automatic lung image segmentation assists doctors in identifying diseases such as lun…