Search Results for author: Fengtao Zhou

Found 12 papers, 8 papers with code

Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts

no code implementations5 Aug 2024 Andong Tan, Fengtao Zhou, Hao Chen

To the best of our knowledge, our "OpenCBM" is the first CBM with concepts of open vocabularies, providing users the unique benefit such as removing, adding, or replacing any desired concept to explain the model's prediction even after a model is trained.

Zero-Shot Learning

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

1 code implementation26 Jul 2024 Jiabo Ma, Zhengrui Guo, Fengtao Zhou, Yihui Wang, Yingxue Xu, Yu Cai, Zhengjie ZHU, Cheng Jin, Yi Lin, Xinrui Jiang, Anjia Han, Li Liang, Ronald Cheong Kin Chan, Jiguang Wang, Kwang-Ting Cheng, Hao Chen

To address this gap, we established a most comprehensive benchmark to evaluate the performance of off-the-shelf foundation models across six distinct clinical task types, encompassing a total of 39 specific tasks.

Representation Learning Self-Knowledge Distillation

A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

no code implementations22 Jul 2024 Yingxue Xu, Yihui Wang, Fengtao Zhou, Jiabo Ma, Shu Yang, Huangjing Lin, Xin Wang, Jiguang Wang, Li Liang, Anjia Han, Ronald Cheong Kin Chan, Hao Chen

To our knowledge, this is the first attempt to incorporate multimodal knowledge at the slide level for enhancing pathology FMs, expanding the modelling context from unimodal to multimodal knowledge and from patch-level to slide-level.

whole slide images

Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

no code implementations28 Jun 2024 Huajun Zhou, Fengtao Zhou, Chenyu Zhao, Yingxue Xu, Luyang Luo, Hao Chen

The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor.

Data Integration Decision Making +1

Post-hoc Part-prototype Networks

no code implementations5 Jun 2024 Andong Tan, Fengtao Zhou, Hao Chen

Therefore, a natural question is: can one construct a network that answers both "where" and "what" in a post-hoc manner to guarantee the model's performance?

Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis

1 code implementation3 Apr 2024 Huajun Zhou, Fengtao Zhou, Hao Chen

In this paper, we propose a Cohort-individual Cooperative Learning (CCL) framework to advance cancer survival analysis by collaborating knowledge decomposition and cohort guidance.

Survival Analysis

Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology

2 code implementations CVPR 2024 Wenhao Tang, Fengtao Zhou, Sheng Huang, Xiang Zhu, Yi Zhang, Bo Liu

Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R$^2$T is tailored to re-embed instance features online.

Multiple Instance Learning Prognosis

Cross-Modal Translation and Alignment for Survival Analysis

1 code implementation ICCV 2023 Fengtao Zhou, Hao Chen

With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images.

Decoder Survival Analysis +2

Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification

1 code implementation ICCV 2023 Wenhao Tang, Sheng Huang, Xiaoxian Zhang, Fengtao Zhou, Yi Zhang, Bo Liu

Moreover, the student is used to update the teacher with an exponential moving average (EMA), which in turn identifies new hard instances for subsequent training iterations and stabilizes the optimization.

Image Classification Multiple Instance Learning

Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation

1 code implementation23 May 2022 Jiazhi Xu, Sheng Huang, Fengtao Zhou, Luwen Huangfu, Daniel Zeng, Bo Liu

Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels.

Knowledge Distillation Multi-Label Image Classification

Deep Semantic Dictionary Learning for Multi-label Image Classification

1 code implementation23 Dec 2020 Fengtao Zhou, Sheng Huang, Yun Xing

Compared with single-label image classification, multi-label image classification is more practical and challenging.

Classification Dictionary Learning +2

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