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Found 13 papers, 1 papers with code
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
The Generalized Fourier Transform: A Unified Framework for the Fourier, Laplace, Mellin and $Z$ Transforms
The DTFT and unilateral $z$-transform are shown to be the special cases of the proposed GDTFT.
eSampling: Energy Harvesting ADCs
We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints.
SDCT-AuxNet$^θ$: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier for Cancer Diagnosis
However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset.
Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer
We also propose an unorthodox ensemble strategy that helps us in providing improved classification over models trained on 7-folds giving a weighted-$F_1$ score of 95. 26% on unseen (test) subjects' data that are, so far, the best results on the C-NMC 2019 dataset for B-ALL classification.
EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping
Most of the existing question answering models can be largely compiled into two categories: i) open domain question answering models that answer generic questions and use large-scale knowledge base along with the targeted web-corpus retrieval and ii) closed domain question answering models that address focused questioning area and use complex deep learning models.
LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer
Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers.
U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool
Automated brain tissue segmentation into white matter (WM), gray matter (GM), and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple sclerosis, etc.
Statistical learning of rational wavelet transform for natural images
Motivated with the concept of transform learning and the utility of rational wavelet transform in audio and speech processing, this paper proposes Rational Wavelet Transform Learning in Statistical sense (RWLS) for natural images.
Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix
This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed (CS) images and used for the reconstruction of the same.
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network
The loss function of the convolutional neural network is setup with total squared error between the given input image to CNN and the reconstructed image at the output of CNN, leading to perfect reconstruction at the end of train- ing.
