Oncology/Hematology

Mechanical Imaging of Soft Tissues With Miniature Climbing Robots.

Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later stage breast cancer and lymphoma manifest themselves as hard nodes under the skin. Currently, mechanical measurements are done manually, with a sense of touch or a handheld tool. Manual measurements do not provide quantitative information and vary depe...

Robustifying Deep Networks for Medical Image Segmentation.

The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade glioma...

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and...

Peak learning of mass spectrometry imaging data using artificial neural networks.

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutic...

Postresection Period-Specific Hazard of Recurrence as a Framework for Surveillance Strategy in Patients with Hepatocellular Carcinoma: A Multicenter Outcome Study.

INTRODUCTION: In spite of the high frequency of recurrence of hepatocellular carcinoma (HCC) after resection, little evidence exists to directly help ...

Robot-Assisted Laparoscopic Management of Bladder/Prostate Rhabdomyosarcoma in Children: Initial Series and 1-Year Outcomes.

To present our preliminary experiences of robot-assisted laparoscopic radical or partial cystectomy for bladder/prostate rhabdomyosarcoma (B/P RMS) i...

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study.

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postop...

An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging.

The intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging (IVIM-DWI) with a series of images with different-values has ...

Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer.

This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving...

functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence.

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and no...

T Cell Epitope Prediction and Its Application to Immunotherapy.

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as...

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.

PURPOSE: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastase...

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning.

Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell car...

Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.

Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although e...

Multiclass classification of whole-body scintigraphic images using a self-defined convolutional neural network with attention modules.

PURPOSE: A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the norm...

Application of green synthesised copper iodide particles on cotton fabric-protective face mask material against COVID-19 pandemic.

Microorganisms cause variety of diseases that constitutes a severe threat to mankind. Due to the upsurge of many infectious diseases, there is a high ...

Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation.

Imaging examination plays an important role in the early diagnosis of myeloma. The study focused on the segmentation effects of deep learning-based mo...

Precise Control of Customized Macrophage Cell Robot for Targeted Therapy of Solid Tumors with Minimal Invasion.

Injecting micro/nanorobots into the body to kill tumors is one of the ultimate ambitions for medical nanotechnology. However, injecting current micro/...

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions.

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was orig...

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge.

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, ...