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Found 7 papers, 1 papers with code
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.
Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation
An accurate and robust quantitative measurement of brain volume change is paramount for translational research and clinical applications.
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.
Scope Head for Accurate Localization in Object Detection
Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance.
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.
Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition.
