Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images.

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres.

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards.

Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types.

Background: Identifying lymph node metastasis (LNM) relies mainly on indirect radiology. Current studies omitted the quantified associations with traits beyond cancer types, failing to provide generalisation performance across various tumour types.

Methods: 4400 whole slide images across 11 cancer types were collected for training, cross-verification, and external validation of the pan-cancer lymph node metastasis (PC-LNM) model.

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading.

Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determine tumor size and Gleason score. This process is highly time-consuming, subjective, and relies on the extensive experience of the pathologists. Deep learning based artificial intelligence shows an ability to match pathologists on many prostate cancer diagnostic scenarios.

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images.

Background: The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations.

Genome-scale signatures of adaptive gene expression changes in an invasive seaweed Gracilaria vermiculophylla.

Invasive species can successfully and rapidly colonize new niches and expand ranges via founder effects and enhanced tolerance towards environmental stresses. However, the underpinning molecular mechanisms (i.e.

Recent Advances in Design Strategies of Tough Hydrogels.

Hydrogels are a fascinating class of materials popular in numerous fields, including tissue engineering, drug delivery, soft robotics, and sensors, thanks to their 3D network porous structure containing a significant amount of water. However, traditional hydrogels exhibit poor mechanical strength, limiting their practical applications. Thus, many researchers have focused on the development of mechanically enhanced hydrogels.

MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography.

Multifrequency electrical impedance tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation, and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging.

A novel hydrogel based on Bletilla striata polysaccharide for rapid hemostasis: Synthesis, characterization and evaluation.

The purpose of this study is to develop a new polysaccharide-based hydrogel. The Box-Behnken design was used to optimize the optimal synthesis conditions of the hydrogel, with the swelling parameters as indicators. The findings of rheologic tests confirm that free radical polymerization and the introduction of linear polymers improved the mechanical strength of the hydrogel.

FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging.

Inverse problems are essential to imaging applications. In this letter, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade.

Multi-Frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency-Constrained Sparse Bayesian Learning.

Imaging the bio-impedance distribution of the brain can provide initial diagnosis of acute stroke. This paper presents a compact and non-radiative tomographic modality, i.e.

Electronics of a Wearable ECG With Level Crossing Sampling and Human Body Communication.

In this paper, the human body communication (HBC) and level crossing sampling (LCS) are combined to design electronics for a wearable electrocardiograph (ECG). The ECG signals acquired by capacitively coupled electrodes are sampled with LCS in place of conventional synchronous sampling. In order to transmit signals through HBC at low frequencies (100 kHz, 1 MHz), an electric field sensor with high input impedance is adopted as the front end of the HBC receiver.

Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes.

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.