RETHINKING INTERMEDIATE LAYERS DESIGN IN KNOWLEDGE DISTILLATION FOR KIDNEY AND LIVER TUMOR SEGMENTATION.

Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, has encountered challenges. Many existing KD methods are not specifically tailored for these tasks. Moreover, prevalent KD methods often lack a careful consideration of 'what' and 'from where' to distill knowledge from the teacher to the student.

Mott memristor based stochastic neurons for probabilistic computing.

Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic leaky integrate and fire (LIF) neuron, utilising a Mott memristor's inherent stochastic switching dynamics.

CLINet: A novel deep learning network for ECG signal classification.

Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this paper, we propose a deep learning network CLINet for ECG signal classification.

PaCL: Patient-aware contrastive learning through metadata refinement for generalized early disease diagnosis.

Early diagnosis plays a pivotal role in effectively treating numerous diseases, especially in healthcare scenarios where prompt and accurate diagnoses are essential. Contrastive learning (CL) has emerged as a promising approach for medical tasks, offering advantages over traditional supervised learning methods. However, in healthcare, patient metadata contains valuable clinical information that can enhance representations, yet existing CL methods often overlook this data.

FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.

Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs.

A Survey of Deep Learning Techniques for Underwater Image Classification.

In recent years, there has been an enormous interest in using deep learning to classify underwater images to identify various objects, such as fishes, plankton, coral reefs, seagrass, submarines, and gestures of sea divers. This classification is essential for measuring the water bodies' health and quality and protecting the endangered species. Furthermore, it has applications in oceanography, marine economy and defense, environment protection, underwater exploration, and human-robot collaborative tasks.

A Survey of Deep Learning on CPUs: Opportunities and Co-Optimizations.

CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL) workloads in systems ranging from mobile to extreme-end servers. In this article, we present a survey of techniques for optimizing DL applications on CPUs. We include the methods proposed for both inference and training and those offered in the context of mobile, desktop/server, and distributed systems.