arXiv subjects

Jaime Cardoso

Publications and source records attributed to Jaime Cardoso.

3 recordsLinked to original sources

arXiv2026

Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma

Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuvant chemotherapy (NACT) followed by delayed primary surgery is commonly employed in patients unsuitable for primary cytoreduction. The Chemotherapy Response Score (CRS) is a validated histopathological biomarker of response to NACT, but it is only available postoperatively. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support adjunct to inform multidisciplinary team (MDT) discussions regarding expected treatment response. Methods. We proposed a 2.5D multimodal deep learning framework that processes lesion-dense omental slices using a pre-trained Vision Transformer encoder and integrates the resulting visual representations with clinical variables through an intermediate fusion module to predict CRS. Results. Our multimodal model, integrating imaging and clinical data, achieved a ROC-AUC of 0.95 alongside 95% accuracy and 80% precision on the internal test cohort (IEO, n=41 patients). On the external test set (OV04, n=70 patients), it achieved a ROC-AUC of 0.68, alongside 67% accuracy and 75% precision. Conclusion. These preliminary results demonstrate the feasibility of transformer-based deep learning for preoperative prediction of CRS in HGSOC using routine clinical data and CT imaging. As an investigational, pre-treatment decision-support tool, this approach may assist MDT discussions by providing early, non-invasive estimates of treatment response.

cs.CV↗

arXiv2024

Phasing segmented telescopes via deep learning methods: application to a deployable CubeSat

Capturing high resolution imagery of the Earth's surface often calls for a telescope of considerable size, even from Low Earth Orbits (LEO). A large aperture often requires large and expensive platforms. For instance, achieving a resolution of 1m at visible wavelengths from LEO typically requires an aperture diameter of at least 30cm. Additionally, ensuring high revisit times often prompts the use of multiple satellites. In light of these challenges, a small, segmented, deployable CubeSat telescope was recently proposed creating the additional need of phasing the telescope's mirrors. Phasing methods on compact platforms are constrained by the limited volume and power available, excluding solutions that rely on dedicated hardware or demand substantial computational resources. Neural Network (NN) are known for their computationally efficient inference and reduced on board requirements. Therefore we developed a NN based method to measure co phasing errors inherent to a deployable telescope. The proposed technique demonstrates its ability to detect phasing error at the targeted performance level (typically a wavefront error (WFE) below 15 nm RMS for a visible imager operating at the diffraction limit) using a point source. The robustness of the NN method is verified in presence of high order aberrations or noise and the results are compared against existing state of the art techniques. The developed NN model ensures its feasibility and provides a realistic pathway towards achieving diffraction limited images.

astro-ph.IM↗

arXiv2007

Matched-filtering and parameter estimation of ringdown waveforms

Using recent results from numerical relativity simulations of non-spinning binary black hole mergers we revisit the problem of detecting ringdown waveforms and of estimating the source parameters, considering both LISA and Earth-based interferometers. We find that Advanced LIGO and EGO could detect intermediate-mass black holes of mass up to about 1000 solar masses out to a luminosity distance of a few Gpc. For typical multipolar energy distributions, we show that the single-mode ringdown templates presently used for ringdown searches in the LIGO data stream can produce a significant event loss (> 10% for all detectors in a large interval of black hole masses) and very large parameter estimation errors on the black hole's mass and spin. We estimate that more than 10^6 templates would be needed for a single-stage multi-mode search. Therefore, we recommend a "two stage" search to save on computational costs: single-mode templates can be used for detection, but multi-mode templates or Prony methods should be used to estimate parameters once a detection has been made. We update estimates of the critical signal-to-noise ratio required to test the hypothesis that two or more modes are present in the signal and to resolve their frequencies, showing that second-generation Earth-based detectors and LISA have the potential to perform no-hair tests.

gr-qc↗