AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Datasets as Topic

Showing 211 to 220 of 1079 articles

Clear Filters

An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs.

BMC medical imaging
BACKGROUND: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short...

Compressing deep graph convolution network with multi-staged knowledge distillation.

PloS one
Given a trained deep graph convolution network (GCN), how can we effectively compress it into a compact network without significant loss of accuracy? Compressing a trained deep GCN into a compact GCN is of great importance for implementing the model ...

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

BMC medical imaging
BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly c...

Deep learning to design nuclear-targeting abiotic miniproteins.

Nature chemistry
There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of ...

Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray.

BMC urology
BACKGROUND: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis usi...

Multi-EPL: Accurate multi-source domain adaptation.

PloS one
Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data...

Machine learning based approach to exam cheating detection.

PloS one
The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of dir...

A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

JAMA network open
IMPORTANCE: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the develop...

MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Biomolecules
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. Howeve...