Search Results for author:
Found 4 papers, 2 papers with code
The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
Finally, our novel sampling scheme reduced sample generation time by a factor of $7$.
Ranked #71 on
Image Classification
on MNIST
Iterative Deep Learning Based Unbiased Stereology With Human-in-the-Loop
In this paper, we introduce an iterative deep learning based method to improve segmentation and counting of cells based on unbiased stereology applied to regions of interest of extended depth of field (EDF) images.
A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection
We also present two methods: a baseline method based on a previously proposed approach, and a deep learning method, and compare their results with other state-of-the-art methods.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
