AIMC Topic: Deep Learning

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Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These m...

Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer ti...

Optimizing Computer-Aided Diagnosis with Cost-Aware Deep Learning Models.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Classical machine learning and deep learning models for Computer-Aided Diagnosis (CAD) commonly focus on overall classification performance, treating misclassification errors (false negatives and false positives) equally during training. This uniform...

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

The Lancet. Digital health
BACKGROUND: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically releva...

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.

Cancer biomarkers : section A of Disease markers
OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

Protein science : a publication of the Protein Society
The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for develo...

A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.

Journal of X-ray science and technology
BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judg...

Lung cancer detection based on computed tomography image using convolutional neural networks.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages.

Deep Learning-based Automated Knee Joint Localization in Radiographic Images Using Faster R-CNN.

Current medical imaging
BACKGROUND: Osteoarthritis is a condition that poses a risk to the knee joint, resulting in pain and impaired function. However, traditional knee X-ray evaluations using the Kellgren-Lawrence grading system have proven to be inefficient. These evalua...

Hybrid CNN-LSTM for Predicting Diabetes: A Review.

Current diabetes reviews
BACKGROUND: Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many ...