Flare Phenomenon After 177 Lu-DOTA-IBA Therapy in Bone Metastases of Lung Cancer.

The flare phenomenon is a transient increase in the number or intensity of lesions on bone scans after treatment, signifying curative effect. DOTA-ibandronic acid (DOTA-IBA) is a new prodrug that targets bone metastases and can be labeled with 177 Lu. Here, we report the case of a 58-year-old woman with bone metastasis, in whom the flare phenomenon was observed after 4 cycles of 177 Lu-DOTA-IBA treatment.

Reliability-Guided Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation.

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability.

Biodistribution and dosimetry of Lu-DOTA-IBA for therapy of bone metastases.

Background: We designed and synthesized a novel bisphosphonate radiopharmaceutical ( Ga- or Lu-labeled DOTA-ibandronate [ Ga/Lu-DOTA-IBA]) for the targeted diagnosis and treatment of bone metastases. The biodistribution and internal dosimetry of a single therapeutic dose of Lu-DOTA-IBA were evaluated using a series of single-photon emission computerized tomography (SPECT) images and blood samples. Five patients with multiple bone metastases were included in this prospective study.

CORE: CORrelation-Guided Feature Enhancement for Few-Shot Image Classification.

Few-shot classification aims to adapt classifiers trained on base classes to novel classes with a few shots. However, the limited amount of training data is often inadequate to represent the intraclass variations in novel classes. This can result in biased estimation of the feature distribution, which in turn results in inaccurate decision boundaries, especially when the support data are outliers.

Theranostic in GLP-1R molecular imaging: challenges and emerging opportunities.

Theranostic in nuclear medicine combines diagnostic imaging and internal irradiation therapy using different therapeutic nuclear probes for visual diagnosis and precise treatment. GLP-1R is a popular receptor target in endocrine diseases, non-alcoholic steatohepatitis, tumors, and other areas. Likewise, it has also made breakthroughs in the development of molecular imaging.

Audio2Gestures: Generating Diverse Gestures From Audio.

People may perform diverse gestures affected by various mental and physical factors when speaking the same sentences. This inherent one-to-many relationship makes co-speech gesture generation from audio particularly challenging. Conventional CNNs/RNNs assume one-to-one mapping, and thus tend to predict the average of all possible target motions, easily resulting in plain/boring motions during inference.

Self-Supervised Tracking via Target-Aware Data Synthesis.

While deep-learning-based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised (SS) learning for visual tracking. In this work, we develop the crop-transform-paste operation, which is able to synthesize sufficient training data by simulating various appearance variations during tracking, including appearance variations of objects and background interference.

Label-Aware Distribution Calibration for Long-Tailed Classification.

Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes. The severe sparseness of training instances for the tail classes is the main challenge, which results in biased distribution estimation during training.

Pedestrian Detection by Exemplar-Guided Contrastive Learning.

Typical methods for pedestrian detection focus on either tackling mutual occlusions between crowded pedestrians, or dealing with the various scales of pedestrians. Detecting pedestrians with substantial appearance diversities such as different pedestrian silhouettes, different viewpoints or different dressing, remains a crucial challenge. Instead of learning each of these diverse pedestrian appearance features individually as most existing methods do, we propose to perform contrastive learning to guide the feature learning in such a way that the semantic distance between pedestrians with different appearances in the learned feature space is minimized to eliminate the appearance diversities, whilst the distance between pedestrians and background is maximized.

Deep-Masking Generative Network: A Unified Framework for Background Restoration From Superimposed Images.

Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to diverse and complicated appearance patterns of noise layers within the image. In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.

Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification.

Person re-identification aims to identify whether pairs of images belong to the same person or not. This problem is challenging due to large differences in camera views, lighting and background. One of the mainstream in learning CNN features is to design loss functions which reinforce both the class separation and intra-class compactness.

Attended End-to-end Architecture for Age Estimation from Facial Expression Videos.

The main challenges of age estimation from facial expression videos lie not only in the modeling of the static facial appearance, but also in the capturing of the temporal facial dynamics. Traditional techniques to this problem focus on constructing handcrafted features to explore the discriminative information contained in facial appearance and dynamics separately. This relies on sophisticated feature-refinement and framework-design.

Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model.

We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared with the prior models for time-series classification such as the hidden conditional random field, our model can model very complex decision boundaries, because the number of latent states grows exponentially with the number of hidden units.