AIMC Topic: Image Processing, Computer-Assisted

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CNN based method for classifying cervical cancer cells in pap smear images.

Scientific reports
The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer early is that it is asymptomatic until it develops ...

Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography.

Scientific reports
The foveola, the central region of the human retina, plays a crucial role in sharp color vision and is challenging to study due to its unique anatomy and technical limitations in imaging. We present ConeMapper, an open-source MATLAB software that int...

Automatically predicting lung tumor invasiveness using deep neural networks.

Medical engineering & physics
Early lung cancer invasive detection is important for further treatment and saving lives. In clinical practice, lung tumor invasiveness (LTI) detection is very challenging, imaging-based automatic prediction algorithms offer a non-invasive approach. ...

Self-supervised deep metric learning for prototypical zero-shot lesion retrieval in placenta whole-slide images.

Computers in biology and medicine
Postnatal adverse outcomes can often be explained and predicted by the pathological evaluation of the placenta after a pregnancy. However, placenta whole-slide image (WSI) analysis is not performed systematically due to the specialized skills require...

A novel hybrid vision UNet architecture for brain tumor segmentation and classification.

Scientific reports
This paper focuses on designing and developing novel architectures termed Hybrid Vision UNet-Encoder Decoder (HVU-ED) segmenter and Hybrid Vision UNet-Encoder (HVU-E) classifier for brain tumor segmentation and classification, respectively. The propo...

De-speckling of medical ultrasound image using metric-optimized knowledge distillation.

Scientific reports
Ultrasound imaging provides real-time views of internal organs, which are essential for accurate diagnosis and treatment. However, speckle noise, caused by wave interactions with tissues, creates a grainy texture that hides crucial details. This nois...

Predicting semantic segmentation quality in laryngeal endoscopy images.

PloS one
Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is fo...

Universal dimensions of visual representation.

Science advances
Do visual neural networks learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they share universal features of natural image processing? We characterized the universal...

Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study.

BMC oral health
BACKGROUND: The integration of artificial intelligence (AI) in dental implant planning has emerged as a transformative approach to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the performance of two object detection models...

Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.

Scientific reports
Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive manual interpretation as well as unavoidable interpreter-dependent variabilities. We propose artific...