AI for better life saving decision in healthcare
The use of artificial intelligence model in healthcare continues to rise in demand as it enables medical professionals to make faster, early, accurate decisions that could save lives such as early cancer detection. As accuracy is critical to prevent wrong diagnosis, training the AI model with a clean, labelled medical images and reports plays a vital role.
Kili is trusted to become their data annotation tool partner
Success stories in healthcare
Scaling up AI for breast cancer screening
Kili has enabled an American scaled up company to improve a model of assisted mammography diagnosis. The algorithm has been further enhanced to accurately interpret mammograms, enabling reliable detection of breast cancer at the level of the best experts.
Enabling innovative bladder cancer detection
Kili is used by a French start up which is developing a new diagnostic tool that is non-invasive and more effective than urinary cytology for the detection of bladder cancer. Kili makes it possible to accelerate the classification of microscope images and to perform semantic segmentation of cancer cells.
Improve productivity with medical speech recognition
Kili helped a speech-to-text solution developer specialize a model on medical (semantic) data to fasten transcription of radiologist reports. The project was conducted in three parts: data sourcing, data annotation and data augmentation.
We ensure accuracy and productivity of medical AI model
Kili technology makes annotating medical images and reports simple and fast. Import DICOM 2D, 3D CT Scan or MRI data, classify, draw bounding boxes, create polygons or segment to identify suspicious spots on the skin, lesions, tumors and brain haemorrhages. Build your training datasets with highly customizable interfaces that allow you to combine tasks to improve productivity.
In today’s world, peta bytes of medical data is digitized in various healthcare institutions, public hospitals, retirement homes, medical clinics, pathology laboratories, etc. Unfortunately, these 2D or 3D images, CT Scan or MRI data are often disordered and unstructured. Unlike standard transactional business data, patient data is not directly usable to build models with machine learning.
These medical records must be annotated for the AI model to create impact. However, doing a manual data annotation can be expensive, laborious, and time-consuming. In addition, healthcare institutions need to put an extra-detailed attention to on the data annotation quality, as wrong diagnosis of a patient data would dangerously affect people’s lives.
This is where data annotation tool such as Kili Technology comes in. The powerful NLP and computer vision features along with customizable interface to perform text and image labelling enable the industrialization of AI in healthcare for endless use cases, from AI-assisted radiology and pathology to the identification of rare or difficult to diagnose diseases.
Create medical training datasets from data annotation
Many researchers around the world are looking to use computer vision models to detect skin cancer, brain tumours and other visually diagnosable diseases. However, creating and training these models requires access to large amounts of annotated medical image data.
It is not a big problem to find certain datasets. You can search for “medical datasets” in your favorite search engine. However, in order for a model to be able to make accurate predictions, it must be trained on a large amount of high-quality data that is specialized in the problem you want to address.
Thus, if you have to deal with a real use case, you will have no choice but to collect data very specific to your use case from a clinic or hospital and label it. Labelling can be expensive and of poor quality. That’s where Kili Technology comes in.
What makes Kili Technology different?
Kili Technology manages DICOM 2D, 3D MRI or CT Scan images, and offers specialized interfaces for all annotation tasks related to medical imaging and NLP: image classification for visual diagnosis, identification of lesions, tumors, cancer cells, entities extraction for medical documents, ocr for medical records and more.
Kili Technology’s state of the art quality management system allow an intensive collaboration and a rigorous review throughout the life of the project to ensure clean, high-quality medical imaging training datasets.
At Kili Technology, you can annotate wherever you want with whomever you want. On premise or in Saas, with your annotators or with our annotators, remotely or in your premises, we adapt to your constraints!
Annotating can be expensive. By allowing the use of online learning, active learning, weakly supervised learning or data augmentation, Kili Technology allows you to drastically reduce the cost of annotation!
Kili Technology has access to a unique network of medical professionals around the world able to accurately translate, transcribe, and annotate medical data, so we can quickly create large, custom medical imaging and NLP training datasets.
Training data interfaces for healthcare
Diagnostic for medical imaging
Add structure to the image with Bounding Box Annotation, Semantic Annotation, Polygon Annotation, Point Annotation, Segment Annotation, Image Classification, and more. We support the DICOM image format for AI in radiology.
Entity Extraction for medical documents
Add structure and semantic information to unstructured text at the document and word level. Take advantage of our weakly supervised learning service to use business rules such as regular expressions and dictionaries to annotate massively before human intervention.
OCR for medical records
Crop parts of the text while saving the text to construct training data. Correct even the most subtle input errors, as for sensitive medical data, even small errors cannot be tolerated.
A last but not least, create your own interfaces for your specific tasks with Kili’s interface builder!
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