AIMC Topic: Humans

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Immunohistochemical biomarker scoring in gastroesophageal cancers: Can computers help us?

Pathology, research and practice
The increasing complexity of cancer diagnostics and treatment selection has placed a growing burden on pathologists, particularly in the evaluation of immunohistochemical (IHC) biomarkers. In gastroesophageal cancers (GEC), both adenocarcinoma and sq...

Multi-view based heterogeneous graph contrastive learning for drug-target interaction prediction.

Journal of biomedical informatics
Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primarily focused...

GatorCLR: Personalized predictions of patient outcomes on electronic health records using self-supervised contrastive graph representation.

Journal of biomedical informatics
OBJECTIVE: Recently, there has been growing interest in analyzing large amounts of Electronic Health Record (EHR) data. Patient outcome prediction is a major area of interest in EHR analysis that focuses on predicting the future health status of pati...

Evaluating the impact of human expertise in human-centered AI: A case study on finger-tapping video analysis for dementia detection.

Computers in biology and medicine
PURPOSE: Human-centered artificial intelligence (AI) plays a crucial role in medical research. This paper evaluates the impact of human expertise in AI systems, using dementia prediction as a case study. Specifically, plasma phospho-tau181 (ptau181) ...

Machine learning-enhanced SERS diagnostics: Accelerating the AI-powered transition from laboratory discoveries to clinical practice.

Computers in biology and medicine
Surface-enhanced Raman spectroscopy (SERS) has emerged as a transformative analytical tool in disease diagnostics, offering unparalleled sensitivity and molecular fingerprinting capabilities. However, its clinical translation is hindered by the inher...

SASWISE-UE: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation.

Computers in biology and medicine
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to...

ViTU-net: A hybrid deep learning model with patch-based LSB approach for medical image watermarking and authentication using a hybrid metaheuristic algorithm.

Computers in biology and medicine
In modern healthcare, telemedicine, health records, and AI-driven diagnostics depend on medical image watermarking to secure chest X-rays for pneumonia diagnosis, ensuring data integrity, confidentiality, and authenticity. A 2024 study found over 70 ...

Direct parametric reconstruction in dynamic PET using deep image prior and a novel parameter magnification strategy.

Computers in biology and medicine
BACKGROUND/PURPOSE: Multiple parametric imaging in positron emission tomography (PET) is challenging due to the noisy dynamic data and the complex mapping to kinetic parameters. Although methods like direct parametric reconstruction have been propose...

Deep learning approaches to surgical video segmentation and object detection: A scoping review.

Computers in biology and medicine
INTRODUCTION: Computer vision (CV) has had a transformative impact in biomedical fields such as radiology, dermatology, and pathology. Its real-world adoption in surgical applications, however, remains limited. We review the current state-of-the-art ...

Current trends in glioma tumor segmentation: A survey of deep learning modules.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND: Multiparametric Magnetic Resonance Imaging (mpMRI) is the gold standard for diagnosing brain tumors, especially gliomas, which are difficult to segment due to their heterogeneity and varied sub-regions. While manual segmentation is time-c...