AIMC Journal:
Medical image analysis
Showing 281 to 290 of 684 articles
Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels.
Medical image analysis
May 24, 2022
Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images...
Meta multi-task nuclei segmentation with fewer training samples.
Medical image analysis
May 18, 2022
Cells/nuclei deliver massive information of microenvironment. An automatic nuclei segmentation approach can reduce pathologists' workload and allow precise of the microenvironment for biological and clinical researches. Existing deep learning models ...
Nucleus classification in histology images using message passing network.
Medical image analysis
May 14, 2022
Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are li...
Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.
Medical image analysis
May 13, 2022
Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but ...
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.
Medical image analysis
May 4, 2022
Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised...
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
Medical image analysis
May 4, 2022
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligenc...
Discriminative error prediction network for semi-supervised colon gland segmentation.
Medical image analysis
Apr 22, 2022
Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation ...
Predicting brain structural network using functional connectivity.
Medical image analysis
Apr 22, 2022
Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and fun...
A multimodal deep learning model for cardiac resynchronisation therapy response prediction.
Medical image analysis
Apr 20, 2022
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model ...
Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images.
Medical image analysis
Apr 17, 2022
Deep learning consistently demonstrates high performance in classifying and segmenting medical images like CT, PET, and MRI. However, compared to these kinds of images, whole slide images (WSIs) of stained tissue sections are huge and thus much less ...
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