AIMC Topic: Image Processing, Computer-Assisted

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Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.

IEEE journal of biomedical and health informatics
Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the...

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 ...

Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images.

IEEE journal of biomedical and health informatics
Stroke is an acute cerebral vascular disease that is likely to cause long-term disabilities and death. Immediate emergency care with accurate diagnosis of computed tomographic (CT) images is crucial for dealing with a hemorrhagic stroke. However, due...

Deep Learning for Diagnosis and Segmentation of Pneumothorax: The Results on the Kaggle Competition and Validation Against Radiologists.

IEEE journal of biomedical and health informatics
Pneumothorax is potentially a life-threatening disease that requires urgent diagnosis and treatment. The chest X-ray is the diagnostic modality of choice when pneumothorax is suspected. The computer-aided diagnosis of pneumothorax has received a dram...

A Multiprocessing Scheme for PET Image Pre-Screening, Noise Reduction, Segmentation and Lesion Partitioning.

IEEE journal of biomedical and health informatics
Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by ra...

Weakly Supervised Histopathology Image Segmentation With Sparse Point Annotations.

IEEE journal of biomedical and health informatics
Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervi...

Simultaneous Direct Depth Estimation and Synthesis Stereo for Single Image Plant Root Reconstruction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Plant roots are the main conduit to its interaction with the physical and biological environment. A 3D root system architecture can provide fundamental and applied knowledge of a plant's ability to thrive, but the construction of 3D structures for th...

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...

Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.

European radiology
OBJECTIVE: To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T...

Arguments for the unsuitability of convolutional neural networks for non-local tasks.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images. Unfortunately, the same architectures perfo...