Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …
A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images
G Wang, X Liu, C Li, Z Xu, J Ruan, H Zhu… - … on Medical Imaging, 2020 - ieeexplore.ieee.org
Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for
accurate diagnosis and follow-up. Deep learning has a potential to automate this task but …
accurate diagnosis and follow-up. Deep learning has a potential to automate this task but …
Stochastic co-teaching for training neural networks with unknown levels of label noise
Label noise hampers supervised training of neural networks. However, data without label
noise is often infeasible to attain, especially for medical tasks. Attaining high-quality medical …
noise is often infeasible to attain, especially for medical tasks. Attaining high-quality medical …
Temporal aggregate representations for long-range video understanding
Future prediction, especially in long-range videos, requires reasoning from current and past
observations. In this work, we address questions of temporal extent, scaling, and level of …
observations. In this work, we address questions of temporal extent, scaling, and level of …
Continual learning on noisy data streams via self-purified replay
Continually learning in the real world must overcome many challenges, among which noisy
labels are a common and inevitable issue. In this work, we present a replay-based continual …
labels are a common and inevitable issue. In this work, we present a replay-based continual …
High-resolution radar road segmentation using weakly supervised learning
Autonomous driving has recently gained lots of attention due to its disruptive potential and
impact on the global economy; however, these high expectations are hindered by strict …
impact on the global economy; however, these high expectations are hindered by strict …
Knowledge distillation for efficient audio-visual video captioning
Automatically describing audio-visual content with texts, namely video captioning, has
received significant attention due to its potential applications across diverse fields. Deep …
received significant attention due to its potential applications across diverse fields. Deep …
One-shot weakly-supervised segmentation in 3D medical images
Deep neural networks typically require accurate and a large number of annotations to
achieve outstanding performance in medical image segmentation. One-shot and weakly …
achieve outstanding performance in medical image segmentation. One-shot and weakly …
Satellite Video Multi-Label Scene Classification With Spatial and Temporal Feature Cooperative Encoding: A Benchmark Dataset and Method
W Guo, S Li, F Chen, Y Sun, Y Gu - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Satellite video multi-label scene classification predicts semantic labels of multiple ground
contents to describe a given satellite observation video, which plays an important role in …
contents to describe a given satellite observation video, which plays an important role in …
Large scale video representation learning via relational graph clustering
Abstract Representation learning is widely applied for various tasks on multimedia data, eg,
retrieval and search. One approach for learning useful representation is by utilizing the …
retrieval and search. One approach for learning useful representation is by utilizing the …