AIMC Topic: Humans

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Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

Journal of cancer research and therapeutics
BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).

Artificial intelligence for early detection of lung cancer in GPs' clinical notes: a retrospective observational cohort study.

The British journal of general practice : the journal of the Royal College of General Practitioners
BACKGROUND: The journey of >80% of patients diagnosed with lung cancer starts in general practice. About 75% of patients are diagnosed when it is at an advanced stage (3 or 4), leading to >80% mortality within 1 year at present. The long-term data in...

Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data.

IEEE transactions on medical imaging
Building deep learning models that can rapidly segment whole slide images (WSIs) using only a handful of training samples remains an open challenge in computational pathology. The difficulty lies in the histological images themselves: many morphologi...

Semi-Supervised Echocardiography Video Segmentation via Adaptive Spatio-Temporal Tensor Semantic Awareness and Memory Flow.

IEEE transactions on medical imaging
Accurate segmentation of cardiac structures in echocardiography videos is vital for diagnosing heart disease. However, challenges such as speckle noise, low spatial resolution, and incomplete video annotations hinder the accuracy and efficiency of se...

Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.

IEEE transactions on medical imaging
Near-infrared spectral tomography (NIRST) is a non-invasive imaging technique that provides functional information about biological tissues. Due to diffuse light propagation in tissue and limited boundary measurements, NIRST image reconstruction pres...

Boosting Convolution With Efficient MLP-Permutation for Volumetric Medical Image Segmentation.

IEEE transactions on medical imaging
Recently, the advent of Vision Transformer (ViT) has brought substantial advancements in 3D benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP) network has regained popularity ...

Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging
The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust g...

Feedback Attention to Enhance Unsupervised Deep Learning Image Registration in 3D Echocardiography.

IEEE transactions on medical imaging
Cardiac motion estimation is important for assessing the contractile health of the heart, and performing this in 3D can provide advantages due to the complex 3D geometry and motions of the heart. Deep learning image registration (DLIR) is a robust wa...

Communication Efficient Federated Learning for Multi-Organ Segmentation via Knowledge Distillation With Image Synthesis.

IEEE transactions on medical imaging
Federated learning (FL) methods for multi-organ segmentation in CT scans are gaining popularity, but generally require numerous rounds of parameter exchange between a central server and clients. This repetitive sharing of parameters between server an...

High-Resolution Maps of Left Atrial Displacements and Strains Estimated With 3D Cine MRI Using Online Learning Neural Networks.

IEEE transactions on medical imaging
The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterization of LA motion and deformation but is lackin...