Search Results for author: Jiamei Sun

Found 6 papers, 2 papers with code

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

no code implementations16 Jan 2023 Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Thomas Watson, Nasir Rajpoot, Mitko Veta, Francesco Ciompi

Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.

Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples

no code implementations24 Oct 2021 Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

In this work, we aim to close this gap by studying a conceptually simple approach to defend few-shot classifiers against adversarial attacks.

Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering

no code implementations9 Dec 2020 Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

In this work, we propose a detection strategy to identify adversarial support sets, aimed at destroying the understanding of a few-shot classifier for a certain class.

Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation

no code implementations21 Jul 2020 Lin Geng Foo, Rui En Ho, Jiamei Sun, Alexander Binder

In this work, we propose a two-step post-processing procedure, Split and Expand, that directly improves the conversion of segmentation maps to instances.

Bias Detection Instance Segmentation +2

Explanation-Guided Training for Cross-Domain Few-Shot Classification

1 code implementation17 Jul 2020 Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder

It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models.

Classification Cross-Domain Few-Shot +1

Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models

1 code implementation4 Jan 2020 Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Alexander Binder

We develop variants of layer-wise relevance propagation (LRP) and gradient-based explanation methods, tailored to image captioning models with attention mechanisms.

Hallucination Image Captioning +2

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