The features of a cell nucleus can reveal much about the health of a cell, but finding the nucleus among the background noise of a tissue sample can be laborious and time-consuming when done manually. Khalifa University researchers are exploring how AI can be leveraged to speed up the process of detecting a cell’s nucleus.

Known as the cell’s ‘command center’, the nucleus is a large organelle that stores the cell’s DNA. The nucleus controls all of the cell’s activities, such as growth and metabolism, using the DNA’s genetic information.

Pathologists use features of the cell nucleus to distinguish benign from malignant cells. They examine tissue samples for cells with increased nuclear size and irregularities in the nuclear membrane or abnormal distribution of chromatin, a substance within the chromosomes in the nucleus.

Artificial intelligence techniques are being investigated to streamline the process of nuclei detection. However, these techniques struggle when the tissue samples are noisy or when nuclei appear crowded.

Dr. Naoufel Werghi, Associate Professor, has collaborated with Dr. Sajid Javed, Assistant Professor, and Dr. Jorge Dias, Professor, to develop a machine learning solution that can sift through the background noise and more easily identify individual nuclei. Their results were published in.

In an ideal world, a sample of tissue would contain only that tissue, but most samples contain a rich mix of several other types. Most normal epithelial cells have nuclei which are round to oval-shaped, most lymphoid cells have round nuclei, while stromal cells have ovoid to spindle-shaped nuclei. Any machine learning algorithm would need to be trained on the specific nucleus shape for the nucleus it is tasked to detect, adding a further layer of training required if the algorithm were to be used in other applications.

“Nucleus detection is a challenging task because of the nuclear clutter and diverse shapes and sizes,” said Dr. Werghi. “Additionally, computation challenges arise because the images analyzed are multi-gigapixel images, and could contain billions of pixels and tens of thousands of cell nuclei.”

A number of potential methods have been reported for automatic detection of cell nuclei, including deep learning methods to train a convolutional neural network (CNN) to generate probability maps of where the cell nuclei are present.

Existing approaches are promising, but they require a significant amount of training data and expensive platforms to cater to the high computational requirements. The model proposed by the research team can be trained using much smaller training datasets that can be executed on a typical desktop computer.

“Our solution uses correlation filters to sort through the data and identify nuclei,” explained Dr. Werghi. “Compared to end-to-end deep learning in previous methods, correlation filters are computationally effective and require significantly less training data. The correlation filters are also flexible and can detect complex and irregular-shaped nuclei without requiring handcrafted features.”

These correlation filters help the algorithm to better discriminate different nuclear components from the non-nuclear regions and also to discern each nucleus from the remaining nuclei where they are clustered.

Constraints are placed on the spatial structure of the nucleus and its local contextual information in the correlation filter framework to handle varying nuclei shapes, texture, and clutter. The first considers the spatial structure of the nucleus, while the second discriminates between the nucleus and the non-nucleus region. Both of these help to reduce the noise in any given sample.

The team plans to explore the strength of correlation filters in analysis classifying cell nuclei and tissue phenotyping problems, such as cancer detection.

This research was funded by Khalifa University Center for Autonomous Robotic Systems (KUCARS).

Jade Sterling
Science Writer
5 July 2021

The post Sifting Through the Noise to Find the Nucleus with Artificial Intelligence appeared first on Khalifa University.

Dr. Majid Khonji, Assistant Professor of Electrical Engineering and Computer Science, presents his research on autonomous vehicles at the IJCAI in Macao, China

Programming self-driving cars so that they can navigate unknown roads safely is perhaps the biggest challenge facing the autonomous vehicle (AV) industry.

Uncertain environments, such as extreme weather and unknown roads, or when errors occur in an AV’s sensors or cameras, pose several technical challenges to an AV’s perception algorithms – the algorithms that allow a car to understand what it “sees.”

“A robust AV perception algorithm should account for different sources of uncertainty and should provide a probabilistic view of the world that captures what is unknown in the environment. Such a probabilistic view is essential to generate control policies that are quantifiably safe,” says Dr. Majid Khonji, Assistant Professor of Electrical Engineering and Computer Science and member of KU’s Center for Autonomous Robotic Systems (KUCARS).

However, current state-of-the-art methods for dealing with safety are experimental and are not backed by a rigorous theoretical foundation, Dr. Khonji explained. This means, AVs are trained through repetition until someone decides it is statistically safe, “which is biased towards the test environment and doesn’t give strong theoretical guarantees on safety.”

In August, Dr. Khonji presented a paper at this year’s International Joint Conference on Artificial Intelligence (IJCAI), which is one of the leading global conferences on Artificial Intelligence, on the algorithms he is developing to help robots and AVs handle uncertainty in a much safer, more predictable way. His algorithms enable an AV to plan safe trajectories under uncertainties so that the probability of collision is below a given threshold. His findings are being implemented in a collaborative research project with MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and another with the Korea Advanced Institute of Science and Technology (KAIST) through the KU-KAIST Joint Research Center.

Dr. Khonji is working with KU researchers Dr. Jorge Dias, Professor of Electrical Engineering and Computer Science, and Dr. Lakmal Seneviratne, Professor of Robotics and Director of the Khalifa University Center for Autonomous Robotic Systems (KUCARS).

“Our algorithm is more rigorous and gives a theoretical guarantee on safety,” Dr. Khonji explained. A lack of proper theoretical treatment for the problem of planning under uncertainty may be the reason why autonomous vehicles are yet to fully take off in urban centers around the world. High-profile AV accidents reveal serious safety issues, but Dr. Khonji believes there is a solution that can help prevent serious accidents and AV mishaps, and it lies in the math.

Dr. Khonji and his team proposed a software stack, or chain of algorithms, designed to enable AVs to determine uncertainty from the environment through the three key subsystems of perception, prediction, and planning and control.

Their rigorous algorithms and mathematical models directly address the problem of trajectory optimization under uncertainty, which is considered in its simplest form an NP-Hard problem – a set of problems that have no optimal solution within a reasonable running time – through the best “close-to-optimal approximation algorithm attainable in theory.”

Erica Solomon
Senior Editor
7 October 2019

The post How Self-Driving Cars are Learning to Plan Under Uncertainty appeared first on Khalifa University.