Narmin Ghaffari Laleh
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View article: Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging
Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging Open
View article: In-context learning enables multimodal large language models to classify cancer pathology images
In-context learning enables multimodal large language models to classify cancer pathology images Open
View article: In-context learning enables multimodal large language models to classify cancer pathology images
In-context learning enables multimodal large language models to classify cancer pathology images Open
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In languag…
View article: Automated curation of large‐scale cancer histopathology image datasets using deep learning
Automated curation of large‐scale cancer histopathology image datasets using deep learning Open
Background Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In rea…
View article: Die kommende Entwicklung großer Sprachmodelle in der Medizin
Die kommende Entwicklung großer Sprachmodelle in der Medizin Open
Große Sprachmodelle (Large Language Models, LLMs) sind Tools der künstlichen Intelligenz (KI), die speziell für die Verarbeitung und Erzeugung von Text trainiert sind. LLMs erregten erhebliche öffentliche Aufmerksamkeit, nachdem ChatGPT vo…
View article: Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma
Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma Open
View article: Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study
Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study Open
View article: The future landscape of large language models in medicine
The future landscape of large language models in medicine Open
View article: Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?
Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans? Open
Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in B…
View article: Adversarial attacks and adversarial robustness in computational pathology
Adversarial attacks and adversarial robustness in computational pathology Open
View article: Erratum to ‘Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology’ Medical Image Analysis, Volume 79, July 2022, 102474
Erratum to ‘Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology’ Medical Image Analysis, Volume 79, July 2022, 102474 Open
View article: Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer
Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer Open
Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a chall…
View article: Medical domain knowledge in domain-agnostic generative AI
Medical domain knowledge in domain-agnostic generative AI Open
View article: Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology Open
View article: Swarm learning for decentralized artificial intelligence in cancer histopathology
Swarm learning for decentralized artificial intelligence in cancer histopathology Open
View article: Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy
Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy Open
View article: Adversarial attacks and adversarial robustness in computational pathology
Adversarial attacks and adversarial robustness in computational pathology Open
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers. AI applications are therefore expected to evolve from academic prototypes to commercial products in the coming years. H…
View article: Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application
Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application Open
View article: Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types
Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types Open
In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinic…
View article: Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?
Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans? Open
Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. (199…
View article: Classical mathematical models for prediction of response to chemotherapy and immunotherapy
Classical mathematical models for prediction of response to chemotherapy and immunotherapy Open
Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real …
View article: Medical domain knowledge in domain-agnostic generative AI
Medical domain knowledge in domain-agnostic generative AI Open
The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state of the art in text-to-image generative artificial intelligence (AI). GLIDE has rich representations, but medical application…
View article: DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology
DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology Open
The interpretation of digitized histopathology images has been transformed thanks to artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from raw image data, extending the capabilities of human exp…
View article: Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review
Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review Open
Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune…
View article: Swarm learning for decentralized artificial intelligence in cancer histopathology
Swarm learning for decentralized artificial intelligence in cancer histopathology Open
Artificial Intelligence (AI) can extract clinically actionable information from medical image data. In cancer histopathology, AI can be used to predict the presence of molecular alterations directly from routine histopathology slides. Howe…
View article: Improving Mathematical Models of Cancer through Game-Theoretic Modelling: A Study in Non-Small Cell Lung Cancer
Improving Mathematical Models of Cancer through Game-Theoretic Modelling: A Study in Non-Small Cell Lung Cancer Open
We examined a dataset of 590 Non-Small Cell Lung Cancer patients treated with either chemotherapy or immunotherapy using a game-theoretic model that includes both the evolution of therapy resistance and a cost of resistance. We tested whet…
View article: Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy
Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy Open
Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real …
View article: Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology
Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology Open
Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterati…
View article: Benchmarking artificial intelligence methods for end-to-end computational pathology
Benchmarking artificial intelligence methods for end-to-end computational pathology Open
Artificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supe…
View article: Trained deep neural networks for MSI/dMMR detection in colorectal cancer histology
Trained deep neural networks for MSI/dMMR detection in colorectal cancer histology Open
These are trained neural network models in PyTorch format to process tessellated images of colorectal cancer histology samples. The input is expected to be 224x224 px RGB image tiles normalized with the Macenko method. The output is a prob…