4,636 research outputs found
Heartbeat Anomaly Detection using Adversarial Oversampling
Cardiovascular diseases are one of the most common causes of death in the
world. Prevention, knowledge of previous cases in the family, and early
detection is the best strategy to reduce this fact. Different machine learning
approaches to automatic diagnostic are being proposed to this task. As in most
health problems, the imbalance between examples and classes is predominant in
this problem and affects the performance of the automated solution. In this
paper, we address the classification of heartbeats images in different
cardiovascular diseases. We propose a two-dimensional Convolutional Neural
Network for classification after using a InfoGAN architecture for generating
synthetic images to unbalanced classes. We call this proposal Adversarial
Oversampling and compare it with the classical oversampling methods as SMOTE,
ADASYN, and RandomOversampling. The results show that the proposed approach
improves the classifier performance for the minority classes without harming
the performance in the balanced classes
Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
Deep learning techniques have shown their superior performance in
dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a
challenging task due to the difficulty of incorporating the useful
dermatologist clinical knowledge into the learning process. In this paper, we
propose a novel knowledge-aware deep framework that incorporates some clinical
knowledge into collaborative learning of two important melanoma diagnosis
tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically,
to exploit the knowledge of morphological expressions of the lesion region and
also the periphery region for melanoma identification, a lesion-based pooling
and shape extraction (LPSE) scheme is designed, which transfers the structure
information obtained from skin lesion segmentation into melanoma recognition.
Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma
recognition to skin lesion segmentation, an effective diagnosis guided feature
fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual
learning mechanism that further promotes the inter-task cooperation, and thus
iteratively improves the joint learning capability of the model for both skin
lesion segmentation and melanoma recognition. Experimental results on two
publicly available skin lesion datasets show the effectiveness of the proposed
method for melanoma analysis.Comment: Pattern Recognitio
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
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