159 research outputs found
Deformable dilated faster R-CNN for universal lesion detection in CT images
A paper in EMBC 202
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
An increasing number of public datasets have shown a marked impact on
automated organ segmentation and tumor detection. However, due to the small
size and partially labeled problem of each dataset, as well as a limited
investigation of diverse types of tumors, the resulting models are often
limited to segmenting specific organs/tumors and ignore the semantics of
anatomical structures, nor can they be extended to novel domains. To address
these issues, we propose the CLIP-Driven Universal Model, which incorporates
text embedding learned from Contrastive Language-Image Pre-training (CLIP) to
segmentation models. This CLIP-based label encoding captures anatomical
relationships, enabling the model to learn a structured feature embedding and
segment 25 organs and 6 types of tumors. The proposed model is developed from
an assembly of 14 datasets, using a total of 3,410 CT scans for training and
then evaluated on 6,162 external CT scans from 3 additional datasets. We rank
first on the Medical Segmentation Decathlon (MSD) public leaderboard and
achieve state-of-the-art results on Beyond The Cranial Vault (BTCV).
Additionally, the Universal Model is computationally more efficient (6x faster)
compared with dataset-specific models, generalized better to CT scans from
varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio
Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review
Anomaly detection in medical data is often of critical importance, from diagnosing and potentially localizing disease processes such as epilepsy to detecting and preventing fatal events such as cardiac arrhythmias. Generative adversarial networks (GANs) have since their inception shown promise in various applications and have been shown to be effective in cybersecurity, data denoising, and data augmentation, and have more recently found a potentially important place in the detection of anomalies in medical time series. This chapter provides a selective review of this novel use of GANs, in the process highlighting the nature of anomalies in time series, special challenges related to medical time series, and some general issues in approaching time series anomaly detection with deep learning. We cover the most frequently applied GAN models and briefly detail the current landscape of applying GANs to anomaly detection in two commonly used medical time series, electrocardiography (ECG) and electroencephalography (EEG)
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