AIMC Topic: Algorithms

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CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.

Sensors (Basel, Switzerland)
This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted q...

DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.

International journal of molecular sciences
Enhancers are short genomic segments located in non-coding regions of the genome that play a critical role in regulating the expression of target genes. Despite their importance in transcriptional regulation, effective methods for classifying enhance...

Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction.

BMC medical imaging
Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and precise diagnosis. The enhancement of automated method to detect MI diseases from Normal patients can play a crucial role in healthcare. This paper p...

Using deep learning and word embeddings for predicting human agreeableness behavior.

Scientific reports
The latest advancements of deep learning have resulted in a new era of natural language processing. The machines now possess an unparallel ability to interpret and engage with various tasks such as text classification, content generation and natural ...

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images.

Scientific reports
Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinfo...

How to identify patient perception of AI voice robots in the follow-up scenario? A multimodal identity perception method based on deep learning.

Journal of biomedical informatics
OBJECTIVES: Post-discharge follow-up stands as a critical component of post-diagnosis management, and the constraints of healthcare resources impede comprehensive manual follow-up. However, patients are less cooperative with AI follow-up calls or may...

Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images.

IEEE transactions on medical imaging
Medical image analysis poses significant challenges due to limited availability of clinical data, which is crucial for training accurate models. This limitation is further compounded by the specialized and labor-intensive nature of the data annotatio...

Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to- Aberration Approach.

IEEE transactions on medical imaging
One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which disturbs the transmitted waves and prevents coherent summation of echo...

Multi-Label Chest X-Ray Image Classification With Single Positive Labels.

IEEE transactions on medical imaging
Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduc...

STAR-RL: Spatial-Temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution.

IEEE transactions on medical imaging
Pathology image are essential for accurately interpreting lesion cells in cytopathology screening, but acquiring high-resolution digital slides requires specialized equipment and long scanning times. Though super-resolution (SR) techniques can allevi...