Oncology/Hematology

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.

Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kid...

Advances in mass spectrometry imaging for spatial cancer metabolomics.

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particul...

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.

PURPOSE: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists i...

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with e...

A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma.

BACKGROUND: Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation a...

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' progn...

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual ana...

Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study.

BACKGROUND: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tu...

Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia.

INTRODUCTION: Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes ...

An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images.

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing b...

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagn...

Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence.

Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision...

Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms.

Anaplastic lymphoma kinase (ALK) and ROS oncogene 1 (ROS1) gene fusions are well-established key players in non-small cell lung cancer (NSCLC). Althou...

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug.

The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards dev...

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches.

Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radio...

Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.

BACKGROUND: Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usab...

Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.

Automatic liver tumor segmentation could offer assistance to radiologists in liver tumor diagnosis, and its performance has been significantly improve...

Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer.

Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to ...

Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning.

BACKGROUND: Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, ...

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determin...