AIMC Topic: Deep Learning

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S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning.

Computers in biology and medicine
Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakl...

Advancing label-free cell classification with connectome-inspired explainable models and a novel LIVECell-CLS dataset.

Computers in biology and medicine
Deep learning label-free cell imaging has become essential in modern medical applications, enabling precise cell analysis while preserving natural biological functions and structures by removing the need for potentially disruptive staining reagents. ...

Comparative analysis of deep learning methods for breast ultrasound lesion detection and classification.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast ultrasound (BUS) computer-aided diagnosis (CAD) systems aims to perform two major steps: detecting lesions and classifying them as benign or malignant. However, the impact of combining both steps has not been previously addressed. Mor...

Beyond accuracy: The need for explainable AI in biomedical voice technology.

Computers in biology and medicine
Speech and voice have emerged as valuable non-invasive biomarkers for detecting and monitoring a range of medical conditions, from neurodegenerative and respiratory diseases to psychiatric and emotional disorders. Recent advancements in artificial in...

AITom: AI-guided cryo-electron tomography image analyses toolkit.

Journal of structural biology
Cryo-electron tomography (cryo-ET) is an essential tool in structural biology, uniquely capable of visualizing three-dimensional macromolecular complexes within their native cellular environments, thereby providing profound molecular-level insights. ...

QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.

Computers in biology and medicine
We propose a deep learning approach for beat-wise atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. AF, a major cardiac arrhythmia affecting millions globally, requires early detection for optimal treatment outcomes. Current rhyt...

Automatic head and neck tumor segmentation through deep learning and Bayesian optimization on three-dimensional medical images.

Computers in biology and medicine
Medical imaging constitutes critical information in the diagnostic and prognostic evaluation of patients, as it serves to uncover a broad spectrum of pathologies and deviances. Clinical practitioners who carry out medical image screening are primaril...

Improving skin lesion classification through saliency-guided loss functions.

Computers in biology and medicine
Deep learning has significantly advanced computer-aided diagnosis, particularly in skin lesion classification. However, achieving high classification performance and providing explainable model predictions remain challenging in medical imaging. To ta...

Deep-Diffeomorphic Networks for Conditional Brain Templates.

Human brain mapping
Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates (e.g., age-specific templates) have advantages over single population templates by enabling improved registration accuracy and capturing common proc...

GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.

Protein science : a publication of the Protein Society
Accurate identification of angiotensin-I-converting enzyme (ACE) inhibitory peptides is essential for understanding the primary factor regulating the renin-angiotensin system and guiding the development of new drug candidates. Given the inherent chal...