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Found 15 papers, 5 papers with code
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
Ranked #5 on
Liver Segmentation
on LiTS2017
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans
We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
A Non-monotonic Smooth Activation Function
Activation functions are crucial in deep learning models since they introduce non-linearity into the networks, allowing them to learn from errors and make adjustments, which is essential for learning complex patterns.
Forecasting formation of a Tropical Cyclone Using Reanalysis Data
In this study, a deep learning model has been proposed that can forecast the formation of a tropical cyclone with a lead time of up to 60 hours with high accuracy.
Smooth Maximum Unit: Smooth Activation Function for Deep Networks Using Smoothing Maximum Technique
Deep learning researchers have a keen interest in proposing new novel activation functions that can boost neural network performance.
SMU: smooth activation function for deep networks using smoothing maximum technique
A good choice of activation function can have significant consequences in improving network performance.
SAU: Smooth activation function using convolution with approximate identities
Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin.
ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions
An activation function is a crucial component of a neural network that introduces non-linearity in the network.
Intensity Prediction of Tropical Cyclones using Long Short-Term Memory Network
Therefore, the prediction of the intensity of tropical cyclones advance in time is of utmost importance.
Tropical cyclone intensity estimations over the Indian ocean using Machine Learning
We use multi-class classification models for the categorical outcome variable, cyclone grade, and regression models for MSWS as it is a continuous variable.
Orthogonal-Padé Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks
We have proposed orthogonal-Pad\'e activation functions, which are trainable activation functions and show that they have faster learning capability and improves the accuracy in standard deep learning datasets and models.
Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone
The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy.
Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data
Landfall of a tropical cyclone is the event when it moves over the land after crossing the coast of the ocean.
EIS -- a family of activation functions combining Exponential, ISRU, and Softplus
In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets.
TanhSoft -- a family of activation functions combining Tanh and Softplus
Deep learning at its core, contains functions that are composition of a linear transformation with a non-linear function known as activation function.
