Mounia Mikram

Background: Clinical imaging is an important part of health care providing physicians with great assistance in patients treatment. In fact, segmentation and grading of tumors can help doctors assess the severity of the cancer at an early stage and increase the chances of cure. Despite that Deep Learning for cancer diagnosis has achieved clinically acceptable accuracy, there still remains challenging tasks, especially in the context of insufficient labeled data and the subsequent need for expensive computational ressources. Objective: This paper presents a lightweight classification and segmentation deep learning model to assist in the identification of cancerous tumors with high accuracy despite the scarcity of medical data. Methods: We propose a multi-task architecture for classification and segmentation of cancerous tumors in the Brain, Skin, Prostate and lungs. The model is based on the UNet architecture with different pre-trained deep learning models (VGG 16 and MobileNetv2) as a backbone. The multi-task model is validated on relatively small datasets (slightly exceed 1200 images) that are diverse in terms of modalities (IRM, X-Ray, Dermoscopic and Digital Histopathology), number of classes, shapes, and sizes of cancer pathologies using the accuracy and dice coefficient as statistical metrics. Results: Experiments show that the multi-task approach improve the learning efficiency and the prediction accuracy for the segmentation and classification tasks, compared to training the individual models separately. The multi-task architecture reached a classification accuracy of 86%, 90%, 88%, and 87% respectively for Skin Lesion, Brain Tumor, Prostate Cancer and Pneumothorax. For the segmentation tasks we were able to achieve high precisions respectively 95%, 98% for the Skin Lesion and Brain Tumor segmentation and a 99% precise segmentation for both Prostate cancer and Pneumothorax. Proving that the multi-task solution is more efficient than single-task networks.

This review explores the transformative impact of artificial intelligence (AI) in medicine. It discusses the benefits of AI, its core technologies, integration processes, and diverse applications. AI enhances diagnostics, personalizes treatments, and optimizes healthcare operations. Machine learning and deep learning are key AI technologies, while explainable AI ensures transparency. The review emphasizes the integration journey and highlights AI applications, from image diagnosis to telemedicine. Ethical concerns, data privacy, regulations, and algorithmic bias are challenges. The future promises continued innovation, global health equity, and responsible AI application in medicine.

Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications.

The diagnosis of spinal deformities is one of the most frequent daily clinical routine. X-ray images are used to diagnose several pathologies in order to reduce harmful radiations of the patient. Spinal deformities are diagnosed essentially from vertebral shapes, orientations, and positions, so their detection and segmentation are major steps required for diagnosis. Deep learning could be applied for automatic diagnosis to detect scoliosis and its variants with a favourable performance. In this study, based on 609 spinal anterior-posterior x-ray images obtained from the public SpineWeb, we examine generative ad- versarial network (GAN) based architectures and convolutional neural network (CNN) based architectures models that are capable of automatically detecting anomalies in radiograph and achieve expert-level performances in various fields providing a solid comparative study. Most of the implemented models are apt to automatically distinguish limits between vertebrae so determining their shape with a very good visual performance. The GAN-based architecture estimates the required vertebral landmarks with an accuracy rate of 0.966, signify its capacity for automatic scoliosis assessment in a clinical setting.

Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.