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Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm.
Publication Type: Academic Journal
Source(s): BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2023 Sep 09; Vol. 23 (1), pp. 178. Date of Electronic Publication: 2023 Sep 09.
Abstract: Background: Food frequency questionnaires (FFQs) are one of the most useful tools for studying and understanding diet-disease relationships. However, because FFQs are self-reported data, they are susceptible to response bias, social desirability bias, ...
A novel collaborative self-supervised learning method for radiomic data.
Publication Type: Academic Journal
Source(s): NeuroImage [Neuroimage] 2023 Aug 15; Vol. 277, pp. 120229. Date of Electronic Publication: 2023 Jun 14.
Abstract: The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this w...
Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.
Publication Type: Academic Journal
Source(s): Magnetic resonance in medicine [Magn Reson Med] 2023 Nov; Vol. 90 (5), pp. 2052-2070. Date of Electronic Publication: 2023 Jul 10.
Abstract: Purpose: To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans.Methods: We propose Noise2Recon, a consistency training method for ...
A unified semi-supervised model with joint estimation of graph, soft labels and latent subspace.
Publication Type: Academic Journal
Source(s): Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2023 Sep; Vol. 166, pp. 248-259. Date of Electronic Publication: 2023 Jul 17.
Abstract: Since manually labeling images is expensive and labor intensive, in practice we often do not have enough labeled images to train an effective classifier for the new image classification tasks. The graph-based SSL methods have received more attention in...
Class-Specific Distribution Alignment for semi-supervised medical image classification.
Publication Type: Academic Journal
Source(s): Computers in biology and medicine [Comput Biol Med] 2023 Sep; Vol. 164, pp. 107280. Date of Electronic Publication: 2023 Jul 22.
Abstract: Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this proble...
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.
Publication Type: Academic Journal
Source(s): Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2023 Apr; Vol. 231, pp. 107398. Date of Electronic Publication: 2023 Feb 07.
Abstract: Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on f...
Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach.
Publication Type: Academic Journal
Source(s): Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Jun 09; Vol. 23 (12). Date of Electronic Publication: 2023 Jun 09.
Abstract: Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying...
A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.
Publication Type: Academic Journal
Source(s): BMC bioinformatics [BMC Bioinformatics] 2022 Oct 14; Vol. 23 (1), pp. 422. Date of Electronic Publication: 2022 Oct 14.
Abstract: Background: Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. The...
Weakly supervised histopathology image segmentation with self-attention.
Publication Type: Academic Journal
Source(s): Medical image analysis [Med Image Anal] 2023 May; Vol. 86, pp. 102791. Date of Electronic Publication: 2023 Mar 11.
Abstract: Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming and lab...
Partial label learning: Taxonomy, analysis and outlook.
Publication Type: Academic Journal
Source(s): Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2023 Apr; Vol. 161, pp. 708-734. Date of Electronic Publication: 2023 Feb 16.
Abstract: Partial label learning (PLL) is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the se...