3 papers
cs.AI2026
Benchmarking PNW Model for MedMNIST to 100% Accuracy
Bo Deng
In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acq…
eess.IV2026
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
Jieyun Bai, Yitong Tang, Zihao Zhou +36
Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled dat…
cs.CV2025
Toward Errorless Training ImageNet-1k
Bo Deng, Levi Heath
In this paper, we describe a feedforward artificial neural network trained on the ImageNet 2012 contest dataset [7] with the new method of [5] to an accuracy rate of 98.3% with a 9…