AIMC Topic: Humans

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Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning.

IEEE transactions on medical imaging
Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous lit...

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.

IEEE transactions on medical imaging
4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods ...

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging
Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-cente...

Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach.

F1000Research
Currently, machine learning applications in marketing allow to optimize strategies, personalize experiences and improve decision making. However, there are still several research gaps, so the objective is to examine the research trends in the use of ...

Obesity classification: a comparative study of machine learning models excluding weight and height data.

Revista da Associacao Medica Brasileira (1992)
OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity classification.

The Touch of Artificial Intelligence in Social Work: Analysis of Social Investigation Reports in Child Welfare with ChatGPT.

Journal of evidence-based social work (2019)
PURPOSE: This research aimed to examine the responses given by ChatGPT to seven social investigation reports prepared in the field of children regarding deficiencies, risk factors and protective factors, social work intervention plan and institution ...

Automated Identification of Stroke Thrombolysis Contraindications from Synthetic Clinical Notes: A Proof-of-Concept Study.

Cerebrovascular diseases extra
INTRODUCTION: Timely thrombolytic therapy improves outcomes in acute ischemic stroke. Manual chart review to screen for thrombolysis contraindications may be time-consuming and prone to errors. We developed and tested a large language model (LLM)-bas...

STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network.

PloS one
As the financial market becomes increasingly complex, stock prediction and anomaly data detection have emerged as crucial tasks in financial risk management. However, existing methods exhibit significant limitations in handling the intricate relation...

An efficient detection of Sinkhole attacks using machine learning: Impact on energy and security.

PloS one
In the realm of Wireless Sensor Networks (WSNs), the detection and mitigation of sinkhole attacks remain pivotal for ensuring network integrity and efficiency. This paper introduces SFlexCrypt, an innovative approach tailored to address these securit...

Classification of pulmonary diseases from chest radiographs using deep transfer learning.

PloS one
Pulmonary diseases are the leading causes of disabilities and deaths worldwide. Early diagnosis of pulmonary diseases can reduce the fatality rate. Chest radiographs are commonly used to diagnose pulmonary diseases. In clinical practice, diagnosing p...