Rafael Molina

TL;DR: This paper model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework and develops a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings.

TL;DR: The popular neural network architectures used for imaging tasks are reviewed, offering some insight as to how these deep-learning tools can solve the inverse problem.

TL;DR: In oestrogen receptor-positive, HER2-negative, lymph node-negative patients, multianalyte tests such as urokinase plasminogen activator (uPA)-PAI-1, Oncotype DX, MammaPrint, EndoPredict, Breast Cancer Index (BCI) and Prosigna (PAM50) may be used to predict outcome and aid adjunct therapy decision-making.

TL;DR: Recommendations are presented for the routine clinical use of serum and tissue-based markers in the diagnosis and management of patients with breast cancer and recently validated prognostic markers for lymph node-negative breast cancer patients may be of value in selecting node- negative patients that do not require adjuvant chemotherapy.

TL;DR: All tumor markers showed a clear relationship with tumor stage and histology and therefore enabled a better histological diagnosis and helped in the diagnosis of non-small cell lung cancer.