AIMC Topic: Deep Learning

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Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning.

Toxicologic pathology
Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and l...

Current Status of Artificial Intelligence Use in Colonoscopy.

Digestion
BACKGROUND: Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedur...

Deep learning-based metabolomics data study of prostate cancer.

BMC bioinformatics
As a heterogeneous disease, prostate cancer (PCa) exhibits diverse clinical and biological features, which pose significant challenges for early diagnosis and treatment. Metabolomics offers promising new approaches for early diagnosis, treatment, and...

The development of a waste management and classification system based on deep learning and Internet of Things.

Environmental monitoring and assessment
Waste sorting is a key part of sustainable development. To maximize the recovery of resources and reduce labor costs, a waste management and classification system is established. In the system, we use Internet of Things (IoT) and edge computing to im...

ConoDL: a deep learning framework for rapid generation and prediction of conotoxins.

Journal of computer-aided molecular design
Conotoxins, being small disulfide-rich and bioactive peptides, manifest notable pharmacological potential and find extensive applications. However, the exploration of conotoxins' vast molecular space using traditional methods is severely limited, nec...

Histopathological domain adaptation with generative adversarial networks: Bridging the domain gap between thyroid cancer histopathology datasets.

PloS one
Deep learning techniques are increasingly being used to classify medical imaging data with high accuracy. Despite this, due to often limited training data, these models can lack sufficient generalizability to predict unseen test data, produced in dif...

Brain multi modality image inpainting via deep learning based edge region generative adversarial network.

Technology and health care : official journal of the European Society for Engineering and Medicine
A brain tumor (BT) is considered one of the most crucial and deadly diseases in the world, as it affects the central nervous system and its main functions. Headaches, nausea, and balance problems are caused by tumors pressing on nearby brain tissue a...

Progress, Pitfalls, and Impact of AI-Driven Clinical Trials.

Clinical pharmacology and therapeutics
Since the deep learning revolution of the early 2010s, significant efforts and billions of dollars have been invested in applying artificial intelligence (AI) to drug discovery and development (AIDD). However, despite high expectations, few AI-discov...