AIMC Topic: Radiology

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From explanation to intervention: Interactive knowledge extraction from Convolutional Neural Networks used in radiology.

PloS one
Deep Learning models such as Convolutional Neural Networks (CNNs) are very effective at extracting complex image features from medical X-rays. However, the limited interpretability of CNNs has hampered their deployment in medical settings as they fai...

Understanding Bias in Artificial Intelligence: A Practice Perspective.

AJNR. American journal of neuroradiology
In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Society of Neuroradiology (ASNR) Diversity and Inclusion Committee, focused on expanding the understanding of bias in artificial intelligence, with a health...

Radiologists and trainees' perspectives on artificial intelligence.

Radiologia
BACKGROUND AND OBJECTIVES: The purpose of this study was to investigate perspectives held by radiologists on the use of artificial intelligence (AI) in their day-to-day work and to identify factors limiting its routine implementation.

The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND: Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized...

The new era of artificial intelligence in neuroradiology: current research and promising tools.

Arquivos de neuro-psiquiatria
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and ...

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

Japanese journal of radiology
The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Mod...

HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration.

Physics in medicine and biology
This study aims to enhance medical image registration by addressing the limitations of existing approaches that rely on spatial transformations through U-Net, ConvNets, or Transformers. The objective is to develop a novel architecture that combines C...

RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.

Computers in biology and medicine
Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignm...