Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset
We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.
Development & Evaluation of Automated Tumour Monitoring by Image Registration Based on 3D (PET/CT) Images
Tumor tracking in PET/CT is essential for monitoring cancer progression and guiding treatment strategies. Traditionally, nuclear physicians manually track tumors, focusing on the five largest ones (PERCIST criteria), which is both time-consuming and imprecise. Automated tumor tracking can allow matching of the numerous metastatic lesions across scans, enhancing tumor change monitoring.
Robust ML Approach for Screening MET Drug Candidates in Combination with Immune Checkpoint Inhibitors
Present study highlights the significance of dataset size in ICI microbiota models and presents a methodology to enhance the performances of a multi-cohort-based ML approach.
Comparison of Machine Learning Approaches for POD24 Prediction
Early identification of patients with relapsing follicular lymphoma (FL) is critical but remains elusive. We initiated a collaboration between the academic CALYM Carnot Institute aiming at developing interpretable artificial intelligence (AI) models based on PET images to predict POD24.
Padhoc: a Computational Pipeline for Pathway Reconstruction On The Fly
Molecular pathway databases represent cellular processes in a structured and standardized way. These databases support the community-wide utilization of pathway information in biological research and the computational analysis of high-throughput biochemical data. We present Padhoc, a pipeline for pathway ad hoc reconstruction.
2Be3-Net : Combining 2D and 3D convolutional neural networks for 3D PET scans predictions
Radiomics is the main approach used to develop predictive models based on 3D Positron Emission Tomography (PET) scans of patients suffering from cancer. We propose a deep learning architecture associating a 2D feature extractor to a 3D CNN predictor.
Researchers get insights in minutes instead of hours
An international pharmaceutical company sought to unlock the value of its data but lacked the expertise to build a fast, tailored platform, critical for reducing experiment processing from a full day to under an hour.