2 citations · 2 across the 5 of their papers we have counts for
9 papers · 1 filter
Unifying Convolution and Attention via Convolutional Nearest Neighbors
Mingi Kang, Jeová Farias Sales Rocha Neto
Convolutional Neural Networks and Vision Transformers are the two dominant architectural families in computer vision, defined by spatially local convolution and global self-attenti…
Interpolation between Convolution and Attention via K-Nearest Neighbors
Mingi Kang
The shift from Convolutional Neural Networks to Transformers has reshaped computer vision, yet these two architectural families are typically viewed as fundamentally distinct. Conv…
A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities
Ming Kang, Fung Fung Ting, Shier Nee Saw +3
Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentation, which can achieve better segm…
Parallel qMRI Reconstruction from 4x Accelerated Acquisitions
Mingi Kang
Magnetic Resonance Imaging (MRI) acquisitions require extensive scan times, limiting patient throughput and increasing susceptibility to motion artifacts. Accelerated parallel MRI…
PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices
Ming Kang, Fung Fung Ting, Raphaël C. -W. Phan +1
Brain tumor detection in multiplane Magnetic Resonance Imaging (MRI) slices is a challenging task due to the various appearances and relationships in the structure of the multiplan…
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection. In this paper, we develop a novel BGF-YOLO architecture by inco…