AI Medical Compendium Topic

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Specimen Handling

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Automated sample preparation with SP3 for low-input clinical proteomics.

Molecular systems biology
High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot ...

Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT.

Cancer medicine
Tissue micro-morphological abnormalities and interrelated quantitative data can provide immediate evidences for tumorigenesis and metastasis in microenvironment. However, the multiscale three-dimensional nondestructive pathological visualization, mea...

Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet.

Biomedical physics & engineering express
Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection lesions and around normal tissues, and blur...

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Nature communications
Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liqu...

Lidar-Camera Semi-Supervised Learning for Semantic Segmentation.

Sensors (Basel, Switzerland)
In this work, we investigated two issues: (1) How the fusion of lidar and camera data can improve semantic segmentation performance compared with the individual sensor modalities in a supervised learning context; and (2) How fusion can also be levera...

The impact of site-specific digital histology signatures on deep learning model accuracy and bias.

Nature communications
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and dri...

[Automatic segmentation of kidney tumor based on cascaded multiscale convolutional neural networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The background of abdominal computed tomography (CT) images is complex, and kidney tumors have different shapes, sizes and unclear edges. Consequently, the segmentation methods applying to the whole CT images are often unable to effectively segment t...