Connectome-based disentangling of epilepsy networks from insular stereoelectroencephalographic leads.

Objective: Epilepsy is considered as a network disorder of interacting brain regions. The propagation of local epileptic activity from the seizure onset zone (SOZ) along neuronal networks determines the semiology of seizures. However, in highly interconnected brain regions such as the insula, the association between the SOZ and semiology is blurred necessitating invasive stereoelectroencephalography (SEEG).

Mesopredator release moderates trophic control of plant biomass in a Georgia salt marsh.

Predators regulate communities through top-down control in many ecosystems. Because most studies of top-down control last less than a year and focus on only a subset of the community, they may miss predator effects that manifest at longer timescales or across whole food webs. In southeastern US salt marshes, short-term and small-scale experiments indicate that nektonic predators (e.

Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

In this paper we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementary labels that indicate to which class a sample cannot belong to.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study.

Introduction: Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability.

Methods: This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples.