160,259 research outputs found
Collaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially important to
multi-label learning. Most of the existing approaches take label correlations
as prior knowledge, which may not correctly characterize the real relationships
among labels. Besides, label correlations are normally used to regularize the
hypothesis space, while the final predictions are not explicitly correlated. In
this paper, we suggest that for each individual label, the final prediction
involves the collaboration between its own prediction and the predictions of
other labels. Based on this assumption, we first propose a novel method to
learn the label correlations via sparse reconstruction in the label space.
Then, by seamlessly integrating the learned label correlations into model
training, we propose a novel multi-label learning approach that aims to
explicitly account for the correlated predictions of labels while training the
desired model simultaneously. Extensive experimental results show that our
approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Interpretability is essential for machine learning algorithms in high-stakes
application fields such as medical image analysis. However, high-performing
black-box neural networks do not provide explanations for their predictions,
which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc
explanation techniques, which are widely used in practice, have been shown to
suffer from severe conceptual problems. Furthermore, as we show in this paper,
current explanation techniques do not perform adequately in the multi-label
scenario, in which multiple medical findings may co-occur in a single image. We
propose Attri-Net, an inherently interpretable model for multi-label
classification. Attri-Net is a powerful classifier that provides transparent,
trustworthy, and human-understandable explanations. The model first generates
class-specific attribution maps based on counterfactuals to identify which
image regions correspond to certain medical findings. Then a simple logistic
regression classifier is used to make predictions based solely on these
attribution maps. We compare Attri-Net to five post-hoc explanation techniques
and one inherently interpretable classifier on three chest X-ray datasets. We
find that Attri-Net produces high-quality multi-label explanations consistent
with clinical knowledge and has comparable classification performance to
state-of-the-art classification models.Comment: Accepted to MIDL 202
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
The pandemic resulted in vast repositories of unstructured data, including
radiology reports, due to increased medical examinations. Previous research on
automated diagnosis of COVID-19 primarily focuses on X-ray images, despite
their lower precision compared to computed tomography (CT) scans. In this work,
we leverage unstructured data from a hospital and harness the fine-grained
details offered by CT scans to perform zero-shot multi-label classification
based on contrastive visual language learning. In collaboration with human
experts, we investigate the effectiveness of multiple zero-shot models that aid
radiologists in detecting pulmonary embolisms and identifying intricate lung
details like ground glass opacities and consolidations. Our empirical analysis
provides an overview of the possible solutions to target such fine-grained
tasks, so far overlooked in the medical multimodal pretraining literature. Our
investigation promises future advancements in the medical image analysis
community by addressing some challenges associated with unstructured data and
fine-grained multi-label classification.Comment: Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV) Workshops 202
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
Federated learning (FL) enables the collaboration of multiple deep learning
models to learn from decentralized data archives (i.e., clients) without
accessing data on clients. Although FL offers ample opportunities in knowledge
discovery from distributed image archives, it is seldom considered in remote
sensing (RS). In this paper, as a first time in RS, we present a comparative
study of state-of-the-art FL algorithms. To this end, we initially provide a
systematic review of the FL algorithms presented in the computer vision
community for image classification problems, and select several
state-of-the-art FL algorithms based on their effectiveness with respect to
training data heterogeneity across clients (known as non-IID data). After
presenting an extensive overview of the selected algorithms, a theoretical
comparison of the algorithms is conducted based on their: 1) local training
complexity; 2) aggregation complexity; 3) learning efficiency; 4) communication
cost; and 5) scalability in terms of number of clients. As the classification
task, we consider multi-label classification (MLC) problem since RS images
typically consist of multiple classes, and thus can simultaneously be
associated with multi-labels. After the theoretical comparison, experimental
analyses are presented to compare them under different decentralization
scenarios in terms of MLC performance. Based on our comprehensive analyses, we
finally derive a guideline for selecting suitable FL algorithms in RS. The code
of this work will be publicly available at https://git.tu-berlin.de/rsim/FL-RS.Comment: Submitted to the IEEE Geoscience and Remote Sensing Magazin
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