InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language

• URL: http://arxiv.org/abs/2604.21061v1
• Date: Wed, 22 Apr 2026 20:05:37 GMT
• Title: InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language
• Abstract summary: We investigate whether foundational vision-language models can be fine-tuned to predict natural language descriptions of embryo morphology and development.<n>Using a publicly available embryo time-lapse dataset, we fine-tuned PaliGemma-2, a multi-modal vision-language model, with only 1,000 images and corresponding captions.<n>Our results show that the fine-tuned model, InVitroVision, outperformed a commercial model, ChatGPT 5.2, and base models in overall metrics.
• Score: 0.09381376621526816
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The application of artificial intelligence (AI) in IVF has shown promise in improving consistency and standardization of decisions, but often relies on annotated data and does not make use of the multimodal nature of IVF data. We investigated whether foundational vision-language models can be fine-tuned to predict natural language descriptions of embryo morphology and development. Using a publicly available embryo time-lapse dataset, we fine-tuned PaliGemma-2, a multi-modal vision-language model, with only 1,000 images and corresponding captions, describing embryo morphology, embryonic cell cycle and developmental stage. Our results show that the fine-tuned model, InVitroVision, outperformed a commercial model, ChatGPT 5.2, and base models in overall metrics, with performance improving with larger training datasets. This study demonstrates the potential of foundational vision-language models to generalize to IVF tasks with limited data, enabling the prediction of natural language descriptions of embryo morphology and development. This approach may facilitate the use of large language models to retrieve information and scientific evidence from relevant publications and guidelines, and has implications for few-shot adaptation to multiple downstream tasks in IVF.

Related papers

• Beyond Language Modeling: An Exploration of Multimodal Pretraining [125.34714978184638]
  We provide empirical clarity through controlled, from-scratch pretraining experiments.<n>We adopt the Transfusion framework, using next-token prediction for language and diffusion for vision.<n>We demonstrate that the MoE architecture harmonizes this scaling asymmetry by providing the high model capacity required by language.
  arXiv  Detail & Related papers  (2026-03-03T18:58:00Z)
• Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning [25.982757026324474]
  We developed two vision language models, BiomedGPT-Large and BiomedGPT-XLarge, based on an encoder-decoder-based transformer architecture.<n>We fine-tuned the two models on 23 benchmark datasets from 6 multi-modal biomedical tasks.<n>We assessed the zero-shot learning performance and alignment accuracy.
  arXiv  Detail & Related papers  (2025-05-23T03:31:58Z)
• MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding [59.41495657570397]
  We present a comprehensive dataset compiled from Nature Communications articles covering 72 scientific fields.<n>We evaluated 19 proprietary and open-source models on two benchmark tasks, figure captioning and multiple-choice, and conducted human expert annotation.<n>Fine-tuning Qwen2-VL-7B with our task-specific data achieved better performance than GPT-4o and even human experts in multiple-choice evaluations.
  arXiv  Detail & Related papers  (2024-07-06T00:40:53Z)
• Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Models [41.64717254672843]
  Visual grounding occupies a pivotal position in multi-modality vision-language models. We propose ViLaM, a large multi-modality model, that supports multi-tasks of VG. ViLaM extends a wide range of instructions, thereby significantly enhancing its generalization and interaction potentials.
  arXiv  Detail & Related papers  (2023-11-21T03:40:09Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models [0.8878802873945023]
  This study introduces the first systematic study on transferring Vision-Language Models to 2D medical images. Although VLSMs show competitive performance compared to image-only models for segmentation, not all VLSMs utilize the additional information from language prompts.
  arXiv  Detail & Related papers  (2023-08-15T11:28:21Z)
• Customizing General-Purpose Foundation Models for Medical Report Generation [64.31265734687182]
  The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks. We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-06-09T03:02:36Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department [0.03088120935391119]
  We are interested in outcome prediction and patient triage in hospital emergency department based on text information in chief complaints and vital signs recorded at triage. We adapt Perceiver - a modality-agnostic transformer-based model that has shown promising results in several applications. In the experimental analysis, we show that mutli-modality improves the prediction performance compared with models trained solely on text or vital signs.
  arXiv  Detail & Related papers  (2023-04-03T06:32:00Z)
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing [53.89917396428747]
  Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. We explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
  arXiv  Detail & Related papers  (2023-01-11T16:35:33Z)
• UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes [91.24112204588353]
  We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks. We demonstrate the effectiveness of UViM on three diverse and challenging vision tasks.
  arXiv  Detail & Related papers  (2022-05-20T17:47:59Z)
• Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction [0.0]
  We describe a fully annotated dataset of 756 videos of developing embryos, for a total of 337k images. We apply ResNet, LSTM, and ResNet-3D architectures to our dataset and demonstrate that they overperform algorithmic approaches to automatically annotate stage development phases. This is the first step towards deep learning-powered IVF.
  arXiv  Detail & Related papers  (2022-03-01T15:13:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.