Morphological and molecular breast cancer profiling through explainable machine learning

Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain

“…This bias-free computational approach yields insights into the global nature of brain aging as well as pathomechanisms. Finally, due to its generalizability, this approach can be broadly applied across clinical neuroscience, galvanizing the generation of data-driven hypotheses and boosting its applications in personalized medicine (Binder et al, 2021; Esteva et al, 2021; Stenzinger et al, 2021) .…”

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

“…20(a) shows one such application: the task of predicting tissue type from histopathology imagery. The work of [22] proposes a bag-of-words model for the prediction task with invariances to the rotation, shift, and scale of the input data. For the verification of the prediction results, the LRP technique is applied to this model so that heatmaps are produced, offering per-pixel scores that indicate the presence of tumorous structures.…”

“…Finally, as a versatile tool in the sciences, XAI has been allowing to gain novel insights (e.g., [22], [45], [71], [140], [159], [165], [186]) ultimately contributing to further our scientific knowledge.…”

Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle

“…The diagnosis and treatment data of a breast cancer patient can be synchronized in real time through the "Breast Cancer-specific Database System" to form a data file which includes diagnostic imaging data, clinical pathology data, basic patient information, medical advice information, medication, surgery, radiotherapy, chemotherapy, cost settlement, etc., as well as the associated breast cancer knowledge database, etc. Combined with the "multi-modal breast cancer-specific knowledge graph" and based on the databasewide medical big data, various quantitative or qualitative big data machine learning algorithms are utilized for data analysis (20)(21)(22) to output the holographic knowledge portrait analysis reports of the patient's breast cancer risk profile, disease trend, clinical protocol, etc., such as the possibility of certain conclusion and the proportion of certain therapeutic regimen, providing the physicians with multi-dimensional and rich reference information, improving the ability of junior physicians in identification, diagnosis and treatment, and reducing the probability of missed diagnosis and misdiagnosis. Physicians can intuitively view, analyze and integrate multi-dimensional and multi-level holographic knowledge portrait, thus providing reference knowledge for accurate diagnosis and treatment of breast cancer based on the full-volume data.…”