57 research outputs found
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Despite the fact that nonlinear subspace learning techniques (e.g. manifold
learning) have successfully applied to data representation, there is still room
for improvement in explainability (explicit mapping), generalization
(out-of-samples), and cost-effectiveness (linearization). To this end, a novel
linearized subspace learning technique is developed in a joint and progressive
way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning
str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label
classification. The J-Play learns high-level and semantically meaningful
feature representation from high-dimensional data by 1) jointly performing
multiple subspace learning and classification to find a latent subspace where
samples are expected to be better classified; 2) progressively learning
multi-coupled projections to linearly approach the optimal mapping bridging the
original space with the most discriminative subspace; 3) locally embedding
manifold structure in each learnable latent subspace. Extensive experiments are
performed to demonstrate the superiority and effectiveness of the proposed
method in comparison with previous state-of-the-art methods.Comment: accepted in ECCV 201
Hybrid-Fusion Transformer for Multisequence MRI
Medical segmentation has grown exponentially through the advent of a fully
convolutional network (FCN), and we have now reached a turning point through
the success of Transformer. However, the different characteristics of the
modality have not been fully integrated into Transformer for medical
segmentation. In this work, we propose the novel hybrid fusion Transformer
(HFTrans) for multisequence MRI image segmentation. We take advantage of the
differences among multimodal MRI sequences and utilize the Transformer layers
to integrate the features extracted from each modality as well as the features
of the early fused modalities. We validate the effectiveness of our
hybrid-fusion method in three-dimensional (3D) medical segmentation.
Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the
proposed method outperforms previous state-of-the-art methods on the task of
brain tumor segmentation and brain structure segmentation.Comment: 10 pages, 4 figure
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