Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -- this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). Another goal of this paper is to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training.Comment: Submitted to Medical Image Analysi

Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the drawback of this approach is that abstract features incorporating several lesions and areas that are not labelled as a lesion but contain global medically relevant information are thus disregarded: especially for dynamic contrast-enhanced breast MRI, criteria such as background parenchymal enhancement and location within the breast are important for diagnosis and cannot be captured by object detection approaches properly. In this work, we propose a 3D CNN and a multi scale curriculum learning strategy to classify malignancy globally based on an MRI of the whole breast. Thus, the global context of the whole breast rather than individual lesions is taken into account. Our proposed approach does not rely on lesion segmentations, which renders the annotation of training data much more effective than in current object detection approaches. Achieving an AUROC of 0.89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist. Our performance is on par with approaches that, in contrast to our method, rely on pixelwise segmentations of lesions.Comment: Accepted to MICCAI 201

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Applications of computational methods in biomedical breast cancer imaging diagnostics: A review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage

Innovations in Medical Image Analysis and Explainable AI for Transparent Clinical Decision Support Systems

This thesis explores innovative methods designed to assist clinicians in their everyday practice, with a particular emphasis on Medical Image Analysis and Explainability issues. The main challenge lies in interpreting the knowledge gained from machine learning algorithms, also called black-boxes, to provide transparent clinical decision support systems for real integration into clinical practice. For this reason, all work aims to exploit Explainable AI techniques to study and interpret the trained models. Given the countless open problems for the development of clinical decision support systems, the project includes the analysis of various data and pathologies. The main works are focused on the most threatening disease afflicting the female population: Breast Cancer. The works aim to diagnose and classify breast cancer through medical images by taking advantage of a first-level examination such as Mammography screening, Ultrasound images, and a more advanced examination such as MRI. Papers on Breast Cancer and Microcalcification Classification demonstrated the potential of shallow learning algorithms in terms of explainability and accuracy when intelligible radiomic features are used. Conversely, the union of deep learning and Explainable AI methods showed impressive results for Breast Cancer Detection. The local explanations provided via saliency maps were critical for model introspection, as well as increasing performance. To increase trust in these systems and aspire to their real use, a multi-level explanation was proposed. Three main stakeholders who need transparent models have been identified: developers, physicians, and patients. For this reason, guided by the enormous impact of COVID-19 in the world population, a fully Explainable machine learning model was proposed for COVID-19 Prognosis prediction exploiting the proposed multi-level explanation. It is assumed that such a system primarily requires two components: 1) inherently explainable inputs such as clinical, laboratory, and radiomic features; 2) Explainable methods capable of explaining globally and locally the trained model. The union of these two requirements allows the developer to detect any model bias, the doctor to verify the model findings with clinical evidence, and justify decisions to patients. These results were also confirmed for the study of coronary artery disease. In particular machine learning algorithms are trained using intelligible clinical and radiomic features extracted from pericoronaric adipose tissue to assess the condition of coronary arteries. Eventually, some important national and international collaborations led to the analysis of data for the development of predictive models for some neurological disorders. In particular, the predictivity of handwriting features for the prediction of depressed patients was explored. Using the training of neural networks constrained by first-order logic, it was possible to provide high-performance and explainable models, going beyond the trade-off between explainability and accuracy

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd