Developing artificial intelligence methods to help pathologists in analysis of whole slide images for cancer treatment and detection.

Detecting pneumonia using self-explainable methods.

Using deep learning to assess fish stocks from acoustic images.

Working towards more efficient tools for diagnosis support and decision support for diseases using deep learning technologies.‍

Exploiting limited data to perform objective measurements in ultrasound images of the heart.

We collaborate with the Institute of Marine Research (IMR) to develop models and applications to detect and classify fish from echosounders.

Visual intelligence is collaborating with Equinor to develop models that can exploit seismicdata and model the continuity of the subsurface.

Visual Intelligence collaborates with KSAT to improve existing, and develop new algorithms, for vessel detection and object recognition.

Deep learning and AI models must become interpretable, explainable and reliable before they can be utilized in complex domains.

Overcoming the challenges of limited training data in the medical domain and laying the fundamentals for explainability and reliability.

A collaboration with Terratec to develop deep learning methods to automatically detect changes when updating an existing map database.

Visual Intelligence collaborates with KSAT to develop new models for detecting and characterizing oil spills.

Better solutions are needed to estimate the populations of sea mammals, such as breeding seals, from aerial images of the sea ice.