A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to ma…
U-Net network trained for skin lesion segmentation in ISIC challenge.
Melanoma is the deadliest form of skin cancer. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic…
MedAusbild team won ISIC challenge by classifying seven skin diseases.
Efficient skin lesion analysis combines deep CNN and handcrafted features.
Deep learning models outperform dermatologists in skin cancer classification.
Deep learning models can misinterpret skin images, highlighting interpretability challenges.
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to…
In this paper, we demonstrate the potential of applying Variational Autoencoder (VAE) [10] for anomaly detection in skin disease images. VAE is a class of deep generative models which is trained by maximizing the evidence lower bound of data distribution [10]. When trained on only normal data, the resulting model is ab…
This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs us…
BF-VI improves posterior approximation in complex models.
Bayesian model improves cure fraction estimation in survival analysis.
Ensemble methods get better with more models if loss function is convex.
BAR reprograms black-box ML models for transfer learning with scarce data.
Develops a method to estimate average hazard under non-proportional hazards without relying on proportional hazards assumption.
EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.
Deep learning skin lesion classifier explained using CAVs.
In this thesis we present the novel semi-supervised network-based algorithm P-Net, which is able to rank and classify patients with respect to a specific phenotype or clinical outcome under study. The peculiar and innovative characteristic of this method is that it builds a network of samples/patients, where the nodes …
ST-STORM separates semantic and appearance features for robust representation learning.
Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurement…
New DAM method improves AUC scores in medical image classification.