Ehsan Montahaei

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists’ time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists’ time for higher value tasks and reduce errors due to fatigue and subjectivity. (More )

Although GAN-based methods have received many achievements in the last few years, they have not been entirely successful in generating discrete data. The most crucial challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. Despite the fact that several attempts have been made to alleviate this problem, none of the existing GAN-based methods have improved the performance of text generation compared with the maximum likelihood approach in terms of both the quality and the diversity. In this paper, we proposed a new framework for generating discrete data by an adversarial approach in which there is no need to pass the gradient to the generator. The proposed method has an iterative manner in which each new generator is defined based on the last discriminator. It leverages the discreteness of data and the last discriminator to model the real data distribution implicitly. Moreover, the method is supported with theoretical guarantees, and experimental results generally show the superiority of the proposed DGSAN method compared to the other popular or recent methods in generating discrete sequential data.

Although GAN-based methods have received many achievements in the last few years, they have not been entirely successful in generating discrete data. The most crucial challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. (More )

Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generator’s density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.

Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. (More )

Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper, we propose an adversarial framework for classifier training that can also handle imbalanced data. Indeed, a network is trained via an adversarial approach to give weights to samples of the majority class such that the obtained classification problem becomes more challenging for the discriminator and thus boosts its classification capability. In addition to the general imbalanced classification problems, the proposed method can also be used for problems such as graph representation learning in which it is desired to discriminate similar nodes from dissimilar nodes. Experimental results on imbalanced data classification and on the tasks like graph link prediction show the superiority of the proposed method compared to the state-of-the-art methods.

Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper, we propose an adversarial framework for classifier training that can also handle imbalanced data. (More )

The continuous Skyline query has recently become the subject of several research projects due to its wide spectrum of applications such as multi-criteria decision making, graph analysis network, wireless sensor network, and data exploration. In these applications, the datasets are huge and have various dimensions. Moreover, they constantly change as time passes. Therefore, this query is considered as a computation-intensive operation that finding the result in a reasonable time is a challenge. In this paper, we present an efficient parallel continuous Skyline approach. In our suggested method, the dataset points are sorted and pruned based on Manhattan distance. Moreover, we use several optimization methods to optimize memory usage in comparison with naïve implementation. In addition, besides the applied conventional parallelization methods, we partition the time steps based on the number of available cores. The experimental results for a dataset that contains 800k points with 7 dimensions show considerable speedup.

The continuous Skyline query has recently become the subject of several research projects due to its wide spectrum of applications such as multi-criteria decision making, graph analysis network, wireless sensor network, and data exploration. (More )