AIMC Topic: Neoplasms

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High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

IEEE transactions on pattern analysis and machine intelligence
Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide...

Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine.

International journal of oncology
Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combinati...

AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples.

Experimental & molecular medicine
The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual posi...

Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: This study aims to perform a comprehensive systematic review of deep learning (DL) models in predicting RT-induced toxicity.

Improvement of phoswich detector-based β+/γ-ray discrimination algorithm with deep learning.

Medical physics
BACKGROUND: Positron probes can accurately localize malignant tumors by directly detecting positrons emitted from positron-emitting radiopharmaceuticals that accumulate in malignant tumors. In the conventional method for direct positron detection, mu...

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity.

Computers in biology and medicine
Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell a...

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept.

Physical and engineering sciences in medicine
The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the treatment couch using visual markers, followed by fine adjustments to th...