AIMC Topic: Neoplasms

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Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Scientific reports
Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less...

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

Scientific reports
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs o...

Cancer prediction from few amounts of histology samples through self-attention based multi-routines cross-domains network.

Physics in medicine and biology
OBJECTIVE: Rapid and efficient analysis of cancer has become a focus of research. Artificial intelligence can use histopathological data to quickly determine the cancer situation, but still faces challenges. For example, the convolutional network is ...

Evaluation of cancer drug infusion devices prior to the implementation of a compounding robot.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
INTRODUCTION: Compounding robots are increasingly being implemented in hospital pharmacies. In our hospital, the recent acquisition of a robot (RIVA, ARxIUM) for intravenous cancer drug compounding obliged us to replace the previously used infusion d...

Self-Propelled Janus Nanocatalytic Robots Guided by Magnetic Resonance Imaging for Enhanced Tumor Penetration and Therapy.

Journal of the American Chemical Society
Biomedical micro/nanorobots as active delivery systems with the features of self-propulsion and controllable navigation have made tremendous progress in disease therapy and diagnosis, detection, and biodetoxification. However, existing micro/nanorobo...

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.

Military Medical Research
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some pot...

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.

BMC bioinformatics
BACKGROUND: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, whi...

Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI.

European radiology
OBJECTIVES: An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical r...

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.

European radiology experimental
Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmo...