Publications by authors named "Marie-Judith Saint Martin"
Objectives: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).
Materials And Methods: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI.
MRI-based radiomic models have shown promises in predicting the response to neoadjuvant chemotherapy in breast cancer. However, it is difficult to determine which information from the images contributes the most to the prediction: the distribution of gray-levels, the tumour heterogeneity, the shape of the lesions or the intensities of peritumoural regions. The purpose of this study is to dissociate the different sources of information to improve prediction results.
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- The study aimed to create a visual ensemble of deep CNNs to improve 3D segmentation of breast tumors using T1-DCE MRI scans from patients with aggressive breast cancer.
- The methodology involved acquiring multi-center MRI scans, segmenting them by radiologists for training and testing, and using different models to assess segmentation accuracy both quantitatively and qualitatively.
- The results indicated that using subtraction images alongside post-contrast images enhanced segmentation performance, achieving a level of accuracy comparable to inter-radiologist agreement and leading to a significant portion of segmentation regarded as excellent or useful.
Objective: Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies.
View Article and Find Full Text PDFThere has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology.
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