AIMC Topic: Algorithms

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A method for spatial interpretation of weakly supervised deep learning models in computational pathology.

Scientific reports
Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or...

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.

Scientific reports
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource di...

Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.

Scientific reports
Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurrence remains a significant challenge. Traditional methods of assessing patient prognosis, such as AJCC...

Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.

Scientific reports
The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing surviva...

GNNs surpass transformers in tumor medical image segmentation.

Scientific reports
To assess the suitability of Transformer-based architectures for medical image segmentation and investigate the potential advantages of Graph Neural Networks (GNNs) in this domain. We analyze the limitations of the Transformer, which models medical i...

Ensemble of weak spectral total-variation learners: a PET-CT case study.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this, we propose the use of ensembles of weak learners based on spectral total-variation (STV) features (Gilboa G. 2014 A to...

Designing diverse and high-performance proteins with a large language model in the loop.

PLoS computational biology
We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse...

Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease.

PloS one
BACKGROUND: The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.

Adaptive network steganography using deep learning and multimedia video analysis for enhanced security and fidelity.

PloS one
This study presents an advanced adaptive network steganography paradigm that integrates deep learning methodologies with multimedia video analysis to enhance the universality and security of network steganography practices. The proposed approach util...

Identifying dynamic regulation with machine learning using adversarial surrogates.

PloS one
Biological systems maintain stability of their function in spite of external and internal perturbations. An important challenge in studying biological regulation is to identify the control objectives based on empirical data. Very often these objectiv...