AIMC Topic: Image Interpretation, Computer-Assisted

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DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI.

Computers in biology and medicine
A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience...

KS-TMIL: A K-Stage Transformer approach with multiple instance learning model for ovarian cancer subtype classification.

Computers in biology and medicine
Existing multiple instance learning (MIL) methods treat whole slide image (WSI) as collections of independent patches, neglecting these crucial spatial and morphological connections. This paper proposes K-Stage Transformer with multiple instance lear...

Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.

Scientific reports
As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse codin...

AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.

Cardiovascular diabetology
BACKGROUND: Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated ...

SML-Net: Semi-supervised multi-task learning network for carotid plaque segmentation and classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Carotid ultrasound image segmentation and classification are crucial in assessing the severity of carotid plaques which serve as a major cause of ischemic stroke. Although many methods are employed for carotid plaque segmentation and classification, ...

New hybrid features extracted from US images for breast cancer classification.

Scientific reports
Artificial intelligence (AI), and image processing fields play a vital role in classifying benign and malignant breast cancer (BC). The novelty of this paper lies in computing original hybrid features (HF) from textural and shape features of BC integ...

HVUNet: A hybrid vision transformer-based UNet for accurate detection and localization in histopathology images.

Computers in biology and medicine
Precise identification of object of interest (OoI) in histopathology images plays a vital role in cancer diagnosis and prognosis. Despite advances in digital pathology, detecting specific cellular structures within these images remains a significant ...

Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla.

BMC medical imaging
OBJECTIVE: This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifac...

Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers.

Scientific reports
Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of the fetus and image quality fluctuation, its interpretation is quite challenging. Although deep learning include Co...

MTA-Net: Multi-scale triplet attention-aware network for multiclass skin lesion classification.

Computers in biology and medicine
Multiclass classification of skin lesions plays a crucial role in computer-aided skin cancer diagnosis and still remains challenging due to the high similarity between different classes and large variations within the same classes. Additionally, the ...