494 citations · 724 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans. In this paper, we propose a Contextual and Attentional fe…
ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell insta…
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…
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance o…
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
Blood cell detection is a typical small-scale object detection problem in computer vision. In this paper, we propose a CST-YOLO model for blood cell detection based on YOLOv7 archi…