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    Two-Stage approach for semantic image segmentation of breast cancer: deep learning and mass detection in mammographic images
    (CEUR Workshop Proceedings, 2023) Touazi, Faycal; Gaceb, Djamel; Chirane, Marouane; Herzallah, Selma
    Breast cancer is a significant global health problem that predominantly affects women and requires effective screening methods. Mammography, the primary screening approach, presents challenges such as radiologist workload and associated costs. Recent advances in deep learning hold promise for improving breast cancer diagnosis. This paper focuses on early breast cancer detection using deep learning to assist radiologists, reduce their workload and costs. We employed the CBIS-DDSM dataset and various CNN models, including YOLO versions V5, V7, and V8 for mass detection, and transformer-based (nested) models inspired by ViT for mass segmentation. Our diverse approach aims to address the complexity of breast cancer detection and segmentation from medical images. Our results show promise, with a 59% mAP50 for cancer mass detection and an impressive 90.15% Dice coefficient for semantic segmentation. These findings highlight the potential of deep learning to enhance breast cancer diagnosis, paving the way for more efficient and accurate early detection methods.
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    Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
    (Nature Research, 2023) Carla, Sendra-Balcells; Campello, Víctor M.; Torrents-Barrena, Jordina; Ammar, Mohammed
    Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92 ± 0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support
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    Deep Learning Models for Intracranial Hemorrhage Recognition: A comparative study
    (Elsevier, 2022) Ammar, Mohammed; Lamri, Mohamed Amine; Mahmoud, Saïd; Laid, Amel
    Every day, a large number of people with brain injury are received in the emergency rooms. Due to the large number of slices analyzed by the doctors for each patient and to accelerate the diagnosis, the development of a precise computer-aided diagnosis system becomes very recommended. The aim of our work is developing a tool to help radiologists in the detection of intracranial hemorrhage (ICH) and its five (05) subtypes in computed tomography (CT) images. Five deep learning models are tested: ResNet50, VGG16, Xception, InceptionV3 and InceptionResNetV2. Before training these models, preprocessing operations are performed like normalization and windowing. The experiments show that VGG-16 architecture provides the best performances. The model achieves an accuracy of 96%.