6 Result(s)
within
Jeya Maria Jose Valanarasu
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Chapter and Conference Paper
UNeXt: MLP-Based Rapid Medical Image Segmentation Network
UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image segmentation in poi...
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Chapter and Conference Paper
Orientation-Guided Graph Convolutional Network for Bone Surface Segmentation
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound (US)-gui...
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Chapter and Conference Paper
Simultaneous Bone and Shadow Segmentation Network Using Task Correspondence Consistency
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due to minimal and blurred bone s...
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Chapter and Conference Paper
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Over the past decade, deep convolutional neural networks have been widely adopted for medical image segmentation and shown to achieve adequate performance. However, due to inherent inductive biases present in ...
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Chapter and Conference Paper
Over-and-Under Complete Convolutional RNN for MRI Reconstruction
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learning-based methods for MR ima...
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Chapter and Conference Paper
KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations
Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years. However, in our studies, we observe that there is a considerable per...
