Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Doretto, Gianfranco"'
This work addresses how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. We use images with annotated tumor regions to identify a set of tumor patches
Externí odkaz:
http://arxiv.org/abs/2409.13720
Microscopy data collections are becoming larger and more frequent. Accurate and precise quantitative analysis tools like cell instance segmentation are necessary to benefit from them. This is challenging due to the variability in the data, which requ
Externí odkaz:
http://arxiv.org/abs/2402.17165
Autor:
Farrelly, Colleen, Singh, Yashbir, Hathaway, Quincy A., Carlsson, Gunnar, Choudhary, Ashok, Paul, Rahul, Doretto, Gianfranco, Himeur, Yassine, Atalls, Shadi, Mansoor, Wathiq
Institutional bias can impact patient outcomes, educational attainment, and legal system navigation. Written records often reflect bias, and once bias is identified; it is possible to refer individuals for training to reduce bias. Many machine learni
Externí odkaz:
http://arxiv.org/abs/2311.13495
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection
Temporal action detection (TAD) involves the localization and classification of action instances within untrimmed videos. While standard TAD follows fully supervised learning with closed-set setting on large training data, recent zero-shot TAD method
Externí odkaz:
http://arxiv.org/abs/2311.00729
Autor:
Yamazaki, Kashu, Hanyu, Taisei, Vo, Khoa, Pham, Thang, Tran, Minh, Doretto, Gianfranco, Nguyen, Anh, Le, Ngan
Precise 3D environmental mapping is pivotal in robotics. Existing methods often rely on predefined concepts during training or are time-intensive when generating semantic maps. This paper presents Open-Fusion, a groundbreaking approach for real-time
Externí odkaz:
http://arxiv.org/abs/2310.03923
Autor:
Bui, Nhat-Tan, Hoang, Dinh-Hieu, Tran, Minh-Triet, Doretto, Gianfranco, Adjeroh, Donald, Patel, Brijesh, Choudhary, Arabinda, Le, Ngan
Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominen
Externí odkaz:
http://arxiv.org/abs/2309.03493
Recently, deep learning has shown to be effective for Electroencephalography (EEG) decoding tasks. Yet, its performance can be negatively influenced by two key factors: 1) the high variance and different types of corruption that are inherent in the s
Externí odkaz:
http://arxiv.org/abs/2308.11651
ChatGPT is a large language model (LLM) created by OpenAI that has been carefully trained on a large amount of data. It has revolutionized the field of natural language processing (NLP) and has pushed the boundaries of LLM capabilities. ChatGPT has p
Externí odkaz:
http://arxiv.org/abs/2307.04251
Self-supervised learning (SSL) is now a serious competitor for supervised learning, even though it does not require data annotation. Several baselines have attempted to make SSL models exploit information about data distribution, and less dependent o
Externí odkaz:
http://arxiv.org/abs/2307.00651
Current approaches to novelty or anomaly detection are based on deep neural networks. Despite their effectiveness, neural networks are also vulnerable to imperceptible deformations of the input data. This is a serious issue in critical applications,
Externí odkaz:
http://arxiv.org/abs/2306.03331