AIMC Topic: Reproducibility of Results

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Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.

IEEE journal of biomedical and health informatics
Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting ...

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

Scientific reports
To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic ...

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

Scientific reports
Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diag...

Leveraging voxel-wise segmentation uncertainty to improve reliability in assessment of paediatric dysplasia of the hip.

International journal of computer assisted radiology and surgery
PURPOSE: Estimating uncertainty in predictions made by neural networks is critically important for increasing the trust medical experts have in automatic data analysis results. In segmentation tasks, quantifying levels of confidence can provide meani...

Deep learning-enhanced light-field imaging with continuous validation.

Nature methods
Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effect...

An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis.

Human & experimental toxicology
INTRODUCTION: Very little artificial intelligence (AI) work has been performed to investigate acetaminophen-associated hepatotoxicity. The objective of this study was to develop an AI algorithm for analyzing weighted features for toxic hepatitis afte...

Domain adaptation and self-supervised learning for surgical margin detection.

International journal of computer assisted radiology and surgery
PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in s...

Fast and accurate modeling of transient-state, gradient-spoiled sequences by recurrent neural networks.

NMR in biomedicine
Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR fingerprinting. This work uses a new extended phase graph (EPG)-Bloch model for accurate simulation of transient-state, gra...

AI-based pathology predicts origins for cancers of unknown primary.

Nature
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primar...

Against spatial-temporal discrepancy: contrastive learning-based network for surgical workflow recognition.

International journal of computer assisted radiology and surgery
PURPOSE: Automatic workflow recognition from surgical videos is fundamental and significant for developing context-aware systems in modern operating rooms. Although many approaches have been proposed to tackle challenges in this complex task, there a...