Fengling Li

Yongquan Yang, Fengling Li, Yani Wei et al.

In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard treatment option for patients who have locally advanced cancer and some large operable tumors. A patient will have better prognosis when he has achieved a pathological complete response (pCR) with the treatment of NAC. There has been a trend to directly predict pCR to NAC from histological images based on deep learning (DL). However, the DL-based predictive models numerically have better performances in internal validation than in external validation. In this paper, we aim to alleviate this situation with an intrinsic approach. We propose an experts' cognition-driven ensemble deep learning (ECDEDL) approach. Taking the cognition of both pathology and artificial intelligence experts into consideration to improve the generalization of the predictive model to the external validation, ECDEDL can intrinsically approximate the working paradigm of a human being which will refer to his various working experiences to make decisions. ECDEDL was validated with 695 WSIs collected from the same center as the primary dataset to develop the predictive model and perform the internal validation, and was also validated with 340 WSIs collected from other three centers as the external dataset to perform the external validation. In external validation, ECDEDL improves the AUCs of pCR prediction from 61.52(59.80-63.26) to 67.75(66.74-68.80) and the Accuracies of pCR prediction from 56.09(49.39-62.79) to 71.01(69.44-72.58). ECDEDL was quite effective for external validation of predicting pCR to NAC from histological images in breast cancer, numerically approximating the internal validation.

Pengyu Liu, Guohua Dong, Dan Guo et al.

In daily life, we encounter diverse external stimuli, such as images, sounds, and videos. As research in multimodal stimuli and neuroscience advances, fMRI-based brain decoding has become a key tool for understanding brain perception and its complex cognitive processes. Decoding brain signals to reconstruct stimuli not only reveals intricate neural mechanisms but also drives progress in AI, disease treatment, and brain-computer interfaces. Recent advancements in neuroimaging and image generation models have significantly improved fMRI-based decoding. While fMRI offers high spatial resolution for precise brain activity mapping, its low temporal resolution and signal noise pose challenges. Meanwhile, techniques like GANs, VAEs, and Diffusion Models have enhanced reconstructed image quality, and multimodal pre-trained models have boosted cross-modal decoding tasks. This survey systematically reviews recent progress in fMRI-based brain decoding, focusing on stimulus reconstruction from passive brain signals. It summarizes datasets, relevant brain regions, and categorizes existing methods by model structure. Additionally, it evaluates model performance and discusses their effectiveness. Finally, it identifies key challenges and proposes future research directions, offering valuable insights for the field. For more information and resources related to this survey, visit https://github.com/LpyNow/BrainDecodingImage.

Zhekai Du, Xinyao Li, Fengling Li et al.

Conventional Unsupervised Domain Adaptation (UDA) strives to minimize distribution discrepancy between domains, which neglects to harness rich semantics from data and struggles to handle complex domain shifts. A promising technique is to leverage the knowledge of large-scale pre-trained vision-language models for more guided adaptation. Despite some endeavors, current methods often learn textual prompts to embed domain semantics for source and target domains separately and perform classification within each domain, limiting cross-domain knowledge transfer. Moreover, prompting only the language branch lacks flexibility to adapt both modalities dynamically. To bridge this gap, we propose Domain-Agnostic Mutual Prompting (DAMP) to exploit domain-invariant semantics by mutually aligning visual and textual embeddings. Specifically, the image contextual information is utilized to prompt the language branch in a domain-agnostic and instance-conditioned way. Meanwhile, visual prompts are imposed based on the domain-agnostic textual prompt to elicit domain-invariant visual embeddings. These two branches of prompts are learned mutually with a cross-attention module and regularized with a semantic-consistency loss and an instance-discrimination contrastive loss. Experiments on three UDA benchmarks demonstrate the superiority of DAMP over state-of-the-art approaches.