Academic and Research Partnerships
PickCells maintains strategic partnerships with educational and research institutions, strengthening our scientific foundation and expanding our capacity for innovation in AI applied to healthcare.
National Universities
University of Pernambuco (UPE) / CESAR School: PickCells maintains a strategic partnership with UPE and CESAR School for the development of research in Generative AI. UPE is one of the ICTs providing services in the FINEP digital pathology project developed by PickCells.
Federal University of Pernambuco (UFPE): The Computer Science Center (CIN-UFPE) is listed as one of the ICTs providing services in the FINEP early cancer detection project.
Catholic University of Pernambuco (UNICAP): Partner in the early cancer detection system project using artificial intelligence applied to digital pathology.
International Collaborations
University of Arizona (UA): PickCells participates in the "Multi-modal Approach for Predicting Infection Routes in Nursing Homes" project in partnership with College of Public Health/UA and College of Engineering/UA, contributing with expertise in AI applied to healthcare.
UC Davis: Monitoring and tracking viral spread (COVID-19) in nursing homes using machine learning.
ETH Zurich: High-level international research collaborations in AI applied to healthcare.
Large Scale Projects and Funding
FINEP Project
FINEP (Federal public company that promotes science, technology and innovation) in partnership with ICTs (UFPE and UPE).
PickCells project focused on building an early cancer detection system using AI applied to digital pathology (cervical and stomach cancer).
Sabin Investment
Sabin Medicina Diagnóstica was one of PickCells' angel investors in 2020.
Sabin laboratory expressed interest in participating in the multicenter validation phase of the digital pathology system developed in the FINEP project.
Entomology Project
Emprel (Recife City Hall): Contract for MAIA-E (Entomology) solution in counting Aedes Aegypti eggs collected in Ovitrap traps.
Optimization of technicians' time through AI.
Sector Collaborations and R&D-Focused Clients
Entities that are clients or partners in specific cases of AI-based product development.
Nestlé Nutrition
Partnership in developing the Descomplicô Baby platform, which performs multimodal baby health screening through stool analysis.
Prospecting for a new project for personalized nutrition product recommendations, using AI for gynecological patient representatives.
Hospitals and Laboratories
DB Diagnósticos: Collaboration in cervical cancer detection in cytological images using multi-model approach.
HC-FMUSP: Long Covid Analysis and Screening case, focusing on generating scientific correlation through analytics platform.
HCP: Hospital de Câncer de Pernambuco as listed client.
HC-PE / Hospital das Clínicas de Pernambuco
Project focused on operational optimization and high-risk patient triage. Development of Risk Score and Oncological Triage with 95% accuracy in predicting death and cancer risk, plus intelligent queue and bed management.
Vitally Health
Development of solution to support doctors in implementing clinical titration of patients with heart failure, suggesting medication changes and dosages based on wearable data.
TI Saúde
Construction of Datalake related to medical records for centralization and intelligent analysis of medical data, optimizing hospital and laboratory processes.
Scientific Publications
Our research has resulted in publications in scientific journals and prestigious conferences.
Machine Learning Applications in Digital Pathology
Research on machine learning applications in digital pathology, focusing on early cancer detection.
Access full article on Google ScholarA Solution for Counting Aedes aegypti and Aedes albopictus Eggs in Paddles from Ovitraps Using Deep Learning
Solution for automated counting of Aedes aegypti and Aedes albopictus eggs in ovitrap paddles using deep learning, contributing to epidemiological monitoring of arboviruses.
Access full article on Google ScholarAutomated Detection of Patients with ALL: A Literature Review
Literature review on automated detection of Acute Lymphoblastic Leukemia (ALL), identifying important cytomorphological characteristics for automated analysis and comparing specificities of image databases and algorithms.
Access full article on ScienceDirectAvaliando Técnicas de Aprendizado Profundo para Detecção de Esquistossomose Mansoni em Imagens de Exames Parasitológicos
Application of deep learning methods (CNN and SPNN) for automated detection of Schistosomiasis Mansoni eggs in parasitological examinations, with AUC above 0.90.
Access full article on ScienceDirectInterested in Collaborating with Us?
We are always open to new scientific partnerships and research collaborations. Contact us to discuss possible projects.
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