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Eric Cosatto is a Senior Researcher in the Machine Learning Department at NEC Laboratories America in Princeton, NJ. He earned his BS in math from the Lycée Collège Cantonal des Creusets, Sion, and his MS and PhD in Computer Science from EPFL in Lausanne. With decades of experience in AI, he has contributed to foundational systems in handwriting recognition, image segmentation, and biomedical diagnostics.
At NEC Labs, Dr. Cosatto has been instrumental in advancing digital pathology solutions using AI, including deep learning tools for cancer detection and cell classification. His work integrates domain-specific constraints into interpretable neural network models and has appeared in top-tier journals and conferences in computer vision and machine learning. Known for his applied focus, he frequently collaborates with healthcare institutions and domain experts to ensure the clinical utility of AI systems. His research bridges theoretical innovation and real-world impact, supporting NEC’s initiatives in trustworthy and efficient machine learning.
Overview: In problems with a large number of labels, most multi-label and multi-class techniques incur a significant computational burden at test time. This is because, for each test instance, they need to systematically evaluate every label to decide whether it is relevant for the instance or not.
Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered
Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images
In the field of histopathology, computer-aided systems face significant challenges due to diverse domain shifts. They include variations in tissue source organ, preparation and scanningprotocols. These domain shifts can significantly impact algorithms performance in histopathology tasks, such as cancer
The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurategenetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from
This paper presents a framework for real-time concealed weapon detection (CWD) on 3D radar images for walk-through screening systems. The walk-through screening system aims to ensure security in crowded areas by performing CWD on walking persons, hence it requires an accurate and real-time detection
Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis, however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications
In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns
We propose a method to accurately obtain the ratio of tumor cells over an entire histological slide. We use deep fully convolutional neural network models trained to detect and classify cells on images of H&E-stained tissue sections. Pathologists’ labels consisting of exhaustive nuclei locations and
Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction
The higher-order spatial modes of a light beam are receiving significant interest. They can be used to further increase the data speeds of high speed optical communication, and for novel optical sensing modalities. As such, the classification of higher-order spatial modes is ubiquitous. Canonical classification
