15 papers · ranked by Valyu relevance
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen + 5 more
Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a weakly…
Thomas E. Tavolara, Metin N. Gurcan, M. Khalid Khan Niazi, Ognjen Arandjelović
'Ognjen Arandjelović'] Simple Summary Recent AI methods in the automated analysis of histopathological imaging data associated with cancer have trended towards less supervision by humans. Yet, there are circumstances when humans cannot lend a hand to AI. Hence, we present an unsupervised method to learn meaningful…
FRANCISCO JAVIER SÁEZ-MALDONADO, LUZ GARCÍA, LEE A. D. COOPER, JEFFERY A. GOLDSTEIN + 2 more
In the context of histological image classification, Multiple Instance Learning (mil) methods only require labels at Whole Slide Image (wsi) level, effectively reducing the annotation bottleneck. However, for their deployment in real scenarios, they must be able to detect the presence of previously unseen tissues or…
Zhikang Wang, Yue Bi, Tong Pan, Xiaoyu Wang + 7 more
Whole slide imaging, which refers to scanning and converting a complete microscope slide to a digital whole slide image (WSI), is an efficient technique for visualizing tissue sections in disease diagnosis, medical education, and pathological research (; ). In recent years, with the advances in artificial intelligence…
Bita Ghasemkhani, Ozlem Varliklar, Yunus Dogan, Semih Utku + 6 more
Simple Summary This study addresses the classification task in animal science, which helps organize and analyze complex data, essential for making informed decisions. It introduces Federated Multi-Label Learning (FMLL), a novel approach combining federated learning principles with a multi-label learning technique.…
Xiaoli Jiang, Jing Zhou, Xinyue Qiao, Chang Peng + 1 more
In this paper, a novel distance-based multilabel classification algorithm is proposed. The proposed algorithm combines k-nearest neighbors (kNN) with neighborhood classifier (NC) to impose double constraints on the quantity and distance of the neighbors. In short, the radius constraint is introduced in the kNN model to…
Yehuda Nissenbaum, Amichai Painsky
Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. MTL schemes utilize a variety of methods, from traditional linear models to more contemporary deep neural networks. In this work we introduce a novel, highly interpretable, tree-based MTL…
Gideon Kowadlo, Abdelrahman Ahmed, Amir Mayan, David Rawlinson + 1 more
'T. Ganesh Kumar'] Continual learning and few-shot learning are important frontiers in progress toward broader Machine Learning (ML) capabilities. Recently, there has been intense interest in combining both. One of the first examples to do so was the Continual few-shot Learning (CFSL) framework of Antoniou et al.…
Antonios Vogiatzis, Stavros Orfanoudakis, Georgios Chalkiadakis, Konstantia Moirogiorgou + 2 more
'Konstantia Moirogiorgou' 'Michalis Zervakis' 'Loris Nanni'] Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler…
Chaofei Qi, Peng Li, Weiyang Lin, Jie Cao + 1 more
Humans possess unique advantages in dealing with few-shot visual recognition scenarios, inspiring the development of meta-learning methods aimed at emulating these abilities. Current mainstream meta-learning primarily utilizes the monocular vision or the dual asymmetric complementary architectures, collectively…
Berenice Montalvo-Lezama, Gibran Fuentes-Pineda
The limited availability of annotated data presents a major challenge in applying deep learning methods to medical image analysis. Few-shot learning methods aim to recognize new classes from only a few labeled examples. These methods are typically investigated within a standard few-shot learning paradigm, in which all…
M. Priyadharshini, A. Faritha Banu, Bhisham Sharma, Subrata Chowdhury + 3 more
'Subrata Chowdhury' 'Khaled Rabie' 'Thokozani Shongwe' 'Faheem Khan'] In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from…
Zhiqiang Chen, Leelavathi Rajamanickam, Jianfang Cao, Aidi Zhao + 2 more
'Xiaohui Hu' 'Wajid Mumtaz'] This study aims to solve the overfitting problem caused by insufficient labeled images in the automatic image annotation field. We propose a transfer learning model called CNN-2L that incorporates the label localization strategy described in this study. The model consists of an InceptionV3…
Jinghou Ruan, Mingwei Wang, Deqing Liu, Maolin Chen + 2 more
'Friedhelm Schwenker'] In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads to challenges regarding feature selection. As a…
Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang + 1 more
Diseases pose significant threats to crop production, leading to substantial yield reductions and jeopardizing global food security. Timely and accurate detection of crop diseases is essential for ensuring sustainable agricultural development and effective crop management. While deep learning-based computer vision…