321 research outputs found
Abnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma
Sparse Bilinear Logistic Regression
In this paper, we introduce the concept of sparse bilinear logistic
regression for decision problems involving explanatory variables that are
two-dimensional matrices. Such problems are common in computer vision,
brain-computer interfaces, style/content factorization, and parallel factor
analysis. The underlying optimization problem is bi-convex; we study its
solution and develop an efficient algorithm based on block coordinate descent.
We provide a theoretical guarantee for global convergence and estimate the
asymptotical convergence rate using the Kurdyka-{\L}ojasiewicz inequality. A
range of experiments with simulated and real data demonstrate that sparse
bilinear logistic regression outperforms current techniques in several
important applications.Comment: 27 pages, 5 figure
The Neural Networks Based Needle Detection for Medical Retinal Surgery
In recent years, deep learning technology has developed rapidly, and the
application of deep neural networks in the medical image processing field has
become the focus of the spotlight. This paper aims to achieve needle position
detection in medical retinal surgery by adopting the target detection algorithm
based on YOLOv5 as the basic deep neural network model. The state-of-the-art
needle detection approaches for medical surgery mainly focus on needle
structure segmentation. Instead of the needle segmentation, the proposed method
in this paper contains the angle examination during the needle detection
process. This approach also adopts a novel classification method based on the
different positions of the needle to improve the model. The experiments
demonstrate that the proposed network can accurately detect the needle position
and measure the needle angle. The performance test of the proposed method
achieves 4.80 for the average Euclidean distance between the detected tip
position and the actual tip position. It also obtains an average error of 0.85
degrees for the tip angle across all test sets
A surgical system for automatic registration, stiffness mapping and dynamic image overlay
In this paper we develop a surgical system using the da Vinci research kit
(dVRK) that is capable of autonomously searching for tumors and dynamically
displaying the tumor location using augmented reality. Such a system has the
potential to quickly reveal the location and shape of tumors and visually
overlay that information to reduce the cognitive overload of the surgeon. We
believe that our approach is one of the first to incorporate state-of-the-art
methods in registration, force sensing and tumor localization into a unified
surgical system. First, the preoperative model is registered to the
intra-operative scene using a Bingham distribution-based filtering approach. An
active level set estimation is then used to find the location and the shape of
the tumors. We use a recently developed miniature force sensor to perform the
palpation. The estimated stiffness map is then dynamically overlaid onto the
registered preoperative model of the organ. We demonstrate the efficacy of our
system by performing experiments on phantom prostate models with embedded stiff
inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018
Reconstruction of a Long-term spatially Contiguous Solar-Induced Fluorescence (LCSIF) over 1982-2022
Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful
proxy for diagnosing the photosynthetic characteristics of terrestrial
ecosystems. Despite the increasing spatial and temporal resolutions of these
satellite retrievals, records of SIF are primarily limited to the recent
decade, impeding their application in detecting long-term dynamics of ecosystem
function and structure. In this study, we leverage the two surface reflectance
bands (red and near-infrared) available both from Advanced Very High-Resolution
Radiometer (AVHRR, 1982-2022) and MODerate-resolution Imaging Spectroradiometer
(MODIS, 2001-2022). Importantly, we calibrate and orbit-correct the AVHRR bands
against their MODIS counterparts during their overlapping period. Using the
long-term bias-corrected reflectance data, a neural network is then built to
reproduce the Orbiting Carbon Observatory-2 SIF using AVHRR and MODIS, and used
to map SIF globally over the entire 1982-2022 period. Compared with the
previous MODIS-based CSIF product relying on four reflectance bands, our
two-band-based product has similar skill but can be advantageously extended to
the bias-corrected AVHRR period. Further comparison with three widely used
vegetation indices (NDVI, kNDVI, NIRv; all based empirically on red and
near-infrared bands) shows a higher or comparable correlation of LCSIF with
satellite SIF and site-level GPP estimates across vegetation types, ensuring a
greater capacity of LCSIF for representing terrestrial photosynthesis.
Globally, LCSIF-AVHRR shows an accelerating upward trend since 1982, with an
average rate of 0.0025 mW m-2 nm-1 sr-1 per decade during 1982-2000 and 0.0038
mW m-2 nm-1 sr-1 per decade during 2001-2022. Our LCSIF data provide
opportunities to better understand the long-term dynamics of ecosystem
photosynthesis and their underlying driving processes
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