AIMC Topic: Deep Learning

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Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop-specific approaches.

Pest management science
BACKGROUND: Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automate...

Artificial Intelligence-Based Histopathological Subtyping of High-Grade Serous Ovarian Cancer.

The American journal of pathology
Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. However, the accuracy of classification depended on the assessor's experience. This study aimed to deve...

Integrated image-based deep learning and language models for primary diabetes care.

Nature medicine
Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an inte...

Deep coherence learning: An unsupervised deep beamformer for high quality single plane wave imaging in medical ultrasound.

Ultrasonics
Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of tr...

A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques.

Computers in biology and medicine
Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure ...

Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.

NeuroImage
Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, an...

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels.

BMC medical imaging
Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve diagnostic accuracy and treatment effectiveness. Nevertheless, inherent challenges associated with r...

Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam.

BMC medical imaging
Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitatio...

Exploring deep learning strategies for intervertebral disc herniation detection on veterinary MRI.

Scientific reports
Intervertebral Disc Herniation (IVDH) is a common spinal disease in dogs, significantly impacting their health, mobility, and overall well-being. This study initiates an effort to automate the detection and localization of IVDH lesions in veterinary ...

A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.

Scientific reports
The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We ...