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Found 13 papers, 4 papers with code
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
Computational limits to the legibility of the imaged human brain
It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.
mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed.
Optimizing Vision Transformers for Medical Image Segmentation
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.
The Fully Convolutional Transformer for Medical Image Segmentation
To address this shortcoming, we propose The Fully Convolutional Transformer (FCT), which builds on the proven ability of Convolutional Neural Networks to learn effective image representations, and combines them with the ability of Transformers to effectively capture long-term dependencies in its inputs.
Ranked #1 on
Medical Image Segmentation
on ACDC
Rotation Equivariant 3D Hand Mesh Generation from a Single RGB Image
Our rotation equivariant model outperforms state-of-the-art methods on a real-world dataset and we demonstrate that it accurately captures the shape and pose in the generated meshes under rotation of the input hand.
CpT: Convolutional Point Transformer for 3D Point Cloud Processing
It achieves this feat due to its effectiveness in creating a novel and robust attention-based point set embedding through a convolutional projection layer crafted for processing dynamically local point set neighbourhoods.
Survey: Leakage and Privacy at Inference Time
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data.
FatNet: A Feature-attentive Network for 3D Point Cloud Processing
The application of deep learning to 3D point clouds is challenging due to its lack of order.
FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation.
Penalizing small errors using an Adaptive Logarithmic Loss
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth.
SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud Processing
But their application to processing data lying on non-Euclidean domains is still a very active area of research.
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder.
