AIMC Topic: Image Processing, Computer-Assisted

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Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.

Medical & biological engineering & computing
Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between ima...

Application-driven validation of posteriors in inverse problems.

Medical image analysis
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models and Invertible...

Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval.

Medical image analysis
Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular...

Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

Journal of microscopy
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image opt...

Monkeypox diagnosis based on probabilistic K-nearest neighbors (PKNN) algorithm.

Computers in biology and medicine
Although it is not a new illness and has been around since the previous century, monkeypox later resurgence is fraught with difficulties. This study presents a novel approach of diagnosing monkeypox using artificial intelligence, which is called Effe...

Multi-Label Zero-Shot Learning Via Contrastive Label-Based Attention.

International journal of neural systems
Multi-label zero-shot learning (ML-ZSL) strives to recognize all objects in an image, regardless of whether they are present in the training data. Recent methods incorporate an attention mechanism to locate labels in the image and generate class-spec...

DenseSeg: joint learning for semantic segmentation and landmark detection using dense image-to-shape representation.

International journal of computer assisted radiology and surgery
PURPOSE: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for se...

A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography.

NeuroImage
Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are pr...

Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.

Journal of translational medicine
Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists in efficient cancer diagnosis. The remodeled microenvironment and field cancerization may enable th...

An Open-Source, User-Friendly Machine-Learning Method for Automated Segmentation and Analysis of Peripheral Nerve Cross-Sections.

Plastic and reconstructive surgery
BACKGROUND: Quantitative neuromorphometric analysis of the peripheral nerve is paramount to nerve regeneration research. However, this technique relies upon accurate segmentation and determination of myelin and axonal area. Manual histologic analysis...