Our Product
Developed AI-powered software prototype assists pathologists in the differential diagnosis of oncological diseases.
It leverages deep learning (ResNet architecture with residual blocks) to automate the detection of pathological mitoses and other key features on digitized histological slides.
Also it enables high-throughput, automated image analysis, reducing workload, increasing diagnostic accuracy, and minimizing human errors.
Current model characteristics (based on the functional assessment, clinical trial is still to be done):
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Precision = 96%
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Recall = 98%
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Accuracy = 97%
MegAITex
Accurate diagnosis on cancer is one of the most complex areas in clinical oncology due to the rarity, diversity, and heterogeneity of these tumours.
Current diagnostic processes are time-consuming, require significant manual labour, and are subject to expert disagreement, especially in differentiating benign from low-grade malignant tumours.
There is a lack of industrial solutions for automated recognition of morphological and histological images using AI, and no high-quality, open-access datasets for histological image analysis.
Current accuracy is 97% with potential for growth.
What We Do
*Atypical mitosis is a disruption of the normal process of cell division (called mitosis), which leads to abnormal distribution of genetic material between the two new cells. As a result, the daughter cells may receive an unequal number of chromosomes or have chromosomal abnormalities.
Innovative AI model development approach for the atypical mitoses* detections.
A fundamental distinction of our solution are graphics processing technologies and specially developed mathematical methods for object detection with Medical science and experts' engagement.
Our technology uses advanced ML architecture improved and trained as a new model which enhanced and customized for other fields of medicine diagnostics.
Developed mathematical model was tested in State Medical Institution and further fine-tuned to minimize
the number of false positives. For this purpose, individual categories of images that appeared “similar” from the models' perspective were separated into subcategories.
Neural Network Architecture
Technology and Innovation
Neural Network Architecture
Utilizes ResNet-50 and Feature Pyramid Network (FPN) for robust feature extraction and small object detection on large-scale images.
Handles massive histological images (up to 1.5 GB), segments them into manageable tiles for efficient analysis.
Data Processing
Novelty
Focuses on object detection (not just classification or segmentation), overcoming limitations of existing AI models in clinical practice.
Our Consumers
Medical institutions
specializing in diagnostics
Manufacturers
of medical equipment
and analytical systems
Research medical institutions
Our Mission
Revolutionize cancer diagnostics by harnessing the power of advanced AI and medical science to deliver faster, more accurate, and reliable pathology insights.
We are committed to empowering clinicians with cutting-edge tools that reduce diagnostic errors, enhance efficiency, and ultimately improve patient outcomes. By combining innovation in artificial intelligence with deep medical expertise, we aim to close the gap in histopathological analysis and set a new global standard for accessible, scalable, and precise medical diagnostics
Early and accurate diagnostics
improves the patients' quality of life
