Pierre Fillard’s Post

Even a very basic model differentiating benign from malignant will save lots of time. I will go one step further, Even if we had a model to heatmap epithelial areas will be too much helpful. I recommend all friends to follow these series :)

Transforming Breast Cancer Risk Prediction with AI 🤖 Recent advancements in AI are reshaping breast cancer diagnostics. A groundbreaking study in Radiology introduces AsymMirai, an interpretable model that enhances breast cancer risk predictions from mammograms. The Challenge:
Traditional models like Mirai, though effective, are often "black boxes" with opaque decision-making processes. This complexity can lead to overreliance and potential misdiagnoses. The Solution:
AsymMirai simplifies this by using local bilateral dissimilarity to compare left and right breast tissue, offering a clearer and more understandable prediction method. This approach not only maintains high accuracy but also improves transparency, making it easier for radiologists to trust and utilize. What’s Next:
How can such innovations in AI improve diagnostic accuracy across different medical fields? Share your thoughts or experiences! #ArtificialIntelligence #HealthcareAI #BreastCancerAwareness #DeepLearning #MedicalInnovation #Radiology https://lnkd.in/etjMsC6P

Revolutionizing Prostate Cancer Diagnosis with AI! Attend an engaging talk by Dr. Alessandro Caputo, a pathologist passionate about digital pathology. In this Academy session, he explores the groundbreaking role of AI in improving both the diagnosis and treatment of prostate cancer. Learn how AI is transforming pathology workflows, making cancer detection faster and more precise! In this insightful session, you’ll learn: - The three main types of tissue samples pathologists work with: biopsies, transurethral resections (TURP), and prostatectomies—and how AI tools can optimize their analysis.  - How AI algorithms can improve accuracy in identifying prostate cancer while reducing the workload for pathologists.  - The challenges of distinguishing cancerous tissues from non-cancerous ones, and how AI can help avoid misdiagnosis. Whether you're a medical professional, a researcher, or simply curious about AI in healthcare, this lecture offers a deep dive into the future of prostate cancer diagnostics. Ready to explore how AI can reshape pathology? Join us at the ESDIP Academy and revolutionize your understanding of prostate cancer diagnosis with cutting-edge AI insights. Become an ESDIP member and access here: https://lnkd.in/dfTmZqB8 #ProstateCancer #AIInMedicine #PathologyInnovation #HealthcareTechnology #MedicalAI #FutureOfMedicine

Excited to share two of our groundbreaking studies at #ESTRO2024 that showcase the potential of AI and multi-omics data in transforming rectal cancer treatment, all done by the very talented Zhuoyan Shen! Part 1 - Sunday 4:30 PM, Hall 1 Introducing RADIANT: A proof-of-concept database linking multi-omics data from the ARISTOTLE trial for AI-driven predictive modelling in locally advanced rectal cancer (LARC). Key highlights: - Established a comprehensive database integrating clinical, radiotherapy, pathology, and genomic data from 589 LARC patients - Developed AI models to enrich the database, including automatic contouring of anatomical structures and analysis of tumor microenvironment - Enables hypothesis-driven and hypothesis-free analyses to personalize LARC treatment Part 2 - Monday 11:00 AM, Hall 2 A novel AI framework to quantify tumour infiltrating lymphocytes (TILs) and unravel their prognostic significance. Key findings: - Developed and validated an AI framework for automated TIL density quantification from whole slide images - High pre-treatment TIL density significantly associated with improved disease-free and overall survival outcomes - Radiotherapy-induced immune response observed in a subgroup of patients, correlating with better prognosis Join us at both sessions to learn more about these exciting advancements in #MedPhys, #Pathology, #RadOnc, and #AI! We look forward to engaging with you.

We recently hosted a workshop to discuss developments in the use of Artificial Intelligence in breast cancer screening. We welcomed international experts on AI and commercial providers of AI solutions for breast screening to discuss the fundamentals of AI development, practical and technical challenges of implementation and resource requirements. Niall Phelan, Medical Physicist with #BreastCheck talks us through what we learned: https://lnkd.in/eup6qwcG

Researchers developed CHIEF, an AI model that can diagnose and predict prognosis across 19 cancer types using pathology images. The model outperformed existing AI methods in tasks like cancer detection, origin identification, and outcome prediction, achieving up to 94% accuracy across multiple datasets. https://lnkd.in/gCBAtxsU #artificialintelligence #ai #cancer

DeePathology.ai has created the STUDIO, an innovative Do It Yourself Platform for AI solutions creation in pathology. But did you know that we also develop Weakly Supervised methods for making slide-level predictions? In a recent paper that was accepted to MICCAI Society, our CTO Jacob Gildenblat et al. presented an embedding extraction method that combines classical tile-level embedding with an innovative cell-level embedding. The method was validated on H&E stained WSI for human epidermal growth factor receptor 2 status, estrogen receptor status in primary breast cancer, breast cancer metastasis in lymph node tissue, and cell of origin classification in diffuse large B-cell lymphoma. The new method outperformed existing methods and achieved a state-of-the-art performance of 90% area under the receiver operating characteristic curve. Check out the full paper here: https://lnkd.in/dkvwJG98. Want to learn more about Weakly Supervised approaches in pathology? Email Jacob Gildenblat or Chen Sagiv (jacob, [email protected]) to see how we can assist you.

Read a #blog summary about this #research paper #published in Volume 15: "Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN." #Summary ⬇️ https://lnkd.in/eRPUUfyE #cancer #AI #prostatecancer

#BreastCancer is the most common cancer in women of the United States, excluding skin cancers, and represents nearly 1 in 3 new female cancers each year. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many new AI initiatives focus on this. There are, however, many potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. https://ow.ly/JZ9E50RolfT. #AIinBreastImaging #WomensHealthAI #ArtificialIntelligence

Read a #blog summary about this #research paper #published in Volume 15: "Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN." #Summary ⬇️ https://lnkd.in/eRPUUfyE #cancer #AI #prostatecancer