86 research outputs found
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Chest X-rays is one of the most commonly available and affordable
radiological examinations in clinical practice. While detecting thoracic
diseases on chest X-rays is still a challenging task for machine intelligence,
due to 1) the highly varied appearance of lesion areas on X-rays from patients
of different thoracic disease and 2) the shortage of accurate pixel-level
annotations by radiologists for model training. Existing machine learning
methods are unable to deal with the challenge that thoracic diseases usually
happen in localized disease-specific areas. In this article, we propose a
weakly supervised deep learning framework equipped with squeeze-and-excitation
blocks, multi-map transfer, and max-min pooling for classifying thoracic
diseases as well as localizing suspicious lesion regions. The comprehensive
experiments and discussions are performed on the ChestX-ray14 dataset. Both
numerical and visual results have demonstrated the effectiveness of the
proposed model and its better performance against the state-of-the-art
pipelines.Comment: 10 pages. Accepted by the ACM BCB 201
Hierarchical Graph Capsule Network
Graph Neural Networks (GNNs) draw their strength from explicitly modeling the
topological information of structured data. However, existing GNNs suffer from
limited capability in capturing the hierarchical graph representation which
plays an important role in graph classification. In this paper, we innovatively
propose hierarchical graph capsule network (HGCN) that can jointly learn node
embeddings and extract graph hierarchies. Specifically, disentangled graph
capsules are established by identifying heterogeneous factors underlying each
node, such that their instantiation parameters represent different properties
of the same entity. To learn the hierarchical representation, HGCN
characterizes the part-whole relationship between lower-level capsules (part)
and higher-level capsules (whole) by explicitly considering the structure
information among the parts. Experimental studies demonstrate the effectiveness
of HGCN and the contribution of each component.Comment: AAAI 2021; Code: https://github.com/uta-smile/HGC
Bridging the Gap Between Object Detection and User Intent via Query-Modulation
When interacting with objects through cameras, or pictures, users often have
a specific intent. For example, they may want to perform a visual search.
However, most object detection models ignore the user intent, relying on image
pixels as their only input. This often leads to incorrect results, such as lack
of a high-confidence detection on the object of interest, or detection with a
wrong class label. In this paper we investigate techniques to modulate standard
object detectors to explicitly account for the user intent, expressed as an
embedding of a simple query. Compared to standard object detectors,
query-modulated detectors show superior performance at detecting objects for a
given label of interest. Thanks to large-scale training data synthesized from
standard object detection annotations, query-modulated detectors can also
outperform specialized referring expression recognition systems. Furthermore,
they can be simultaneously trained to solve for both query-modulated detection
and standard object detection
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