Oncology/Hematology

Artificial intelligence in cancer research: learning at different levels of data granularity.

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal heal...

A hierarchical three-step superpixels and deep learning framework for skin lesion classification.

Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma...

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive duct...

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing va...

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-sl...

Robot-assisted partial nephrectomy for high-complexity tumors (PADUA score ≥10): Perioperative, long-term functional and oncologic outcomes.

OBJECTIVES: To evaluate the safety and efficacy, and long-term functional and oncologic outcomes of ...

Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.

Pathology reports represent a primary source of information for cancer registries. Hospitals routine...

Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems.

In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tens...

Artificial intelligence in outcomes research: a systematic scoping review.

: Despite the number of systematic reviews of how artificial intelligence is being used in different...

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical...

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays.

Deep learning is becoming increasingly popular and available to new users, particularly in the medic...

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis.

Prospective randomized trials and observational studies have revealed that early detection, classifi...

Clinical practice vs. state-of-the-art research and future visions: Report on the 4D treatment planning workshop for particle therapy - Edition 2018 and 2019.

The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of mo...

Artificial intelligence, machine learning, and drug repurposing in cancer.

: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indic...

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images.

BACKGROUND AND OBJECTIVES: Morphological differentiation among blasts circulating in blood in acute ...

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resist...

Robotic Right Adrenalectomy for Metastatic Sarcoma.

Robotic chemotherapy compounding: A multicenter productivity approach.

INTRODUCTION: The aim of this study is to compare productivity of the KIRO Oncology compounding robo...

Robotic assisted CyberKnife radiosurgery for the treatment of choroidal metastasis.

PURPOSE: Choroidal metastases occur in many patients with systemic cancer and limit quality of life ...

Information retrieval on oncology knowledge base using recursive paraphrase lattice.

For annotation in cancer genomic medicine, oncologists have to refer to various knowledge bases worl...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (...