Hysteroscopy provides direct visualization of the uterine cavity; however, diagnostic outcomes remain highly operator-dependent and subjective. This study aimed to develop an automated deep learning framework based on the ConvNeXt-XLarge architecture, in comparison with an EfficientNetV2 baseline, to objectively classify common uterine pathologies and thereby reduce diagnostic variability. A prospective dataset of 1262 histopathologically confirmed hysteroscopic images was analyzed using an 80:20 training to validation split and a two-stage transfer learning strategy. In addition to the primary multiclass framework, three binary classification models were developed to evaluate pairwise differentiation between the pathologies. The multiclass model achieved an accuracy of 80.5%, a precision of 82.3%, a weighted Area Under the Receiver Operating Characteristic Curve (AUC) of 94.8%, and a weighted average precision (AP) of 91.6%. In the binary classification tasks, model 1 (fibroids vs. functional polyps) achieved an accuracy of 96.7% and an AUC of 99.8%; model 2 (fibroids vs. hyperplastic polyps) achieved 87.2% accuracy and an AUC of 87.4%; and model 3 (functional vs. hyperplastic polyps) achieved 92.2% accuracy and an AUC of 99.0%. ConvNeXt-XLarge statistically significantly outperformed EfficientNetV2 in the multiclass task in terms of accuracy and AUC, and in model 2 in terms of accuracy, underscoring the added value of the deeper backbone for this hysteroscopic image classification. Overall, these findings demonstrate that deep learning frameworks can accurately automate the classification of hysteroscopic findings. By providing high-precision analysis, these models offer a robust tool to mitigate diagnostic subjectivity and optimize clinical decision-making.

The measurement of the strong coupling constant based on four different observables is presented. The four observables are the inclusive jet spectrum, the 3-jet production cross section, the ratio of the inclusive 3-jet cross section to the inclusive 2-jet cross section, and the top quark pair production cross section in the dilepton channel. The data samples used for these measurements were collected at a proton-proton centre-of-mass energy of 7 TeV with the CMS detector at the LHC. The extracted values of the strong coupling constant are in good agreement with the world average value and the running of the strong coupling is confirmed for the first time at the TeV region. Additionally, results on constraining the gluon and the valence-quark PDFs using the inclusive jet spectrum and the muon charge asymmetry in the inclusive W production are also presented.

Measurements on inclusive jet and di-jet cross sections, 3-jet mass cross sections, event shapes and studies on color coherence effects are presented. The data samples used for these measurements were collected during 2011 and 2012 at a proton-proton centre-of-mass energy of 7 and 8 TeV, respectively, with the CMS detector at the LHC. The measurements on inclusive jet, di-jet and 3-jet mass cross sections are compared to the predictions from perturbative QCD at nextto-leading order theoretical calculations. Within uncertainties, data and theory are in agreement. Event shape and color coherence measurements are compared also to various Monte Carlo event generators providing useful information for further tuning of these generators.

Measurements sensitive to QCD multijet production are presented, using data from proton-proton collisions at a centre-of-mass energy of 7 TeV collected with the CMS detector at the CERN LHC. Data were collected during the 2010 data-taking period, and correspond to an integrated luminocity of up to 36 pb-1. The dijet azimuthal decorrelations, the hadronic event shapes and the ratio of the 3-jet to 2-jet production cross-sections confront the QCD multijet dynamics at a previously unexplored kinematic regime. The data are compared to the predictions of various QCD Monte-Carlo generators.