AIMC Topic: Humans

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The Emerging Trends of Multi-Label Learning.

IEEE transactions on pattern analysis and machine intelligence
Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly g...

Hierarchical Bayesian LSTM for Head Trajectory Prediction on Omnidirectional Images.

IEEE transactions on pattern analysis and machine intelligence
When viewing omnidirectional images (ODIs), viewers can access different viewports via head movement (HM), which sequentially forms head trajectories in spatial-temporal domain. Thus, head trajectories play a key role in modeling human attention on O...

Detailed Avatar Recovery From Single Image.

IEEE transactions on pattern analysis and machine intelligence
This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the huma...

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bound...

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

ACS sensors
Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable ...

Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention.

Sensors (Basel, Switzerland)
Today, the rapid development of industrial zones leads to an increased incidence of skin diseases because of polluted air. According to a report by the American Cancer Society, it is estimated that in 2022 there will be about 100,000 people suffering...

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

Journal of translational medicine
BACKGROUND: We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.

Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task.

Nature communications
Primates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesi...

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-Ray Images.

IEEE journal of biomedical and health informatics
Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such as multi-instance learning (MIL) and class activation maps (CAM), howev...

Reliably Filter Drug-Induced Liver Injury Literature With Natural Language Processing and Conformal Prediction.

IEEE journal of biomedical and health informatics
Drug-induced liver injury describes the adverse effects of drugs that damage the liver. Life-threatening results were also reported in severe cases. Therefore, liver toxicity is an important assessment for new drug candidates. These reports are docum...