AIMC Topic: Deep Learning

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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.

Neural networks : the official journal of the International Neural Network Society
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimo...

Gabor wavelet-based deep learning for skin lesion classification.

Computers in biology and medicine
Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early detection and treatment are necessary. Malignant melanoma and seborrheic keratosis are known as commo...

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.

Nature communications
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even f...

Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

Physics in medicine and biology
To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep c...

Cortical Tracking of Surprisal during Continuous Speech Comprehension.

Journal of cognitive neuroscience
Speech comprehension requires rapid online processing of a continuous acoustic signal to extract structure and meaning. Previous studies on sentence comprehension have found neural correlates of the predictability of a word given its context, as well...

How many models/atlases are needed as priors for capturing anatomic population variations?

Medical image analysis
Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Howe...

Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these disea...

Machine learning applications in epilepsy.

Epilepsia
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, lan...

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.

IEEE transactions on cybernetics
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pa...

Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-Contrast CT From Spatiotemporal 4D CT.

IEEE transactions on medical imaging
The imaging workup in acute stroke can be simplified by deriving non-contrast CT (NCCT) from CT perfusion (CTP) images. This results in reduced workup time and radiation dose. To achieve this, we present a stacked bidirectional convolutional LSTM (C-...