Publications
2024
2024
- ACCVTuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation ModelsarXiv preprint arXiv:2410.05239, 2024
Vision-Language Models (VLMs) have shown impressive performance in vision tasks, but adapting them to new domains often requires expensive fine-tuning. Prompt tuning techniques, including textual, visual, and multimodal prompting, offer efficient alternatives by leveraging learnable prompts. However, their application to Vision-Language Segmentation Models (VLSMs) and evaluation under significant domain shifts remain unexplored. This work presents an open-source benchmarking framework, TuneVLSeg, to integrate various unimodal and multimodal prompt tuning techniques into VLSMs, making prompt tuning usable for downstream segmentation datasets with any number of classes. TuneVLSeg includes 6 prompt tuning strategies on various prompt depths used in 2 VLSMs totaling of 8 different combinations. We test various prompt tuning on 8 diverse medical datasets, including 3 radiology datasets (breast tumor, echocardiograph, chest X-ray pathologies) and 5 non-radiology datasets (polyp, ulcer, skin cancer), and two natural domain segmentation datasets. Our study found that textual prompt tuning struggles under significant domain shifts, from natural-domain images to medical data. Furthermore, visual prompt tuning, with fewer hyperparameters than multimodal prompt tuning, often achieves performance competitive to multimodal approaches, making it a valuable first attempt. Our work advances the understanding and applicability of different prompt-tuning techniques for robust domain-specific segmentation.
@article{adhikari2024tunevlseg, title = {TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models}, author = {Adhikari, Rabin and Thapaliya, Safal and Dhakal, Manish and Khanal, Bishesh}, journal = {arXiv preprint arXiv:2410.05239}, year = {2024}, } - MICCAIVLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight BlocksIn International Conference on Medical Image Computing and Computer-Assisted Intervention , 2024
Foundation Vision-Language Models (VLMs) trained using large-scale open-domain images and text pairs have recently been adapted to develop Vision-Language Segmentation Models (VLSMs) that allow providing text prompts during inference to guide image segmentation. If robust and powerful VLSMs can be built for medical images, it could aid medical professionals in many clinical tasks where they must spend substantial time delineating the target structure of interest. VLSMs for medical images resort to fine-tuning base VLM or VLSM pretrained on open-domain natural image datasets due to fewer annotated medical image datasets; this fine-tuning is resource-consuming and expensive as it usually requires updating all or a significant fraction of the pretrained parameters. Recently, lightweight blocks called adapters have been proposed in VLMs that keep the pretrained model frozen and only train adapters during fine-tuning, substantially reducing the computing resources required. We introduce a novel adapter, VLSM-Adapter, that can fine-tune pretrained vision-language segmentation models using transformer encoders. Our experiments in widely used CLIP-based segmentation models show that with only 3 million trainable parameters, the VLSM-Adapter outperforms state-of-the-art and is comparable to the upper bound end-to-end fine-tuning.
@inproceedings{dhakal2024vlsm, title = {VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks}, author = {Dhakal, Manish and Adhikari, Rabin and Thapaliya, Safal and Khanal, Bishesh}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages = {712--722}, year = {2024}, organization = {Springer}, } - MIDLExploring Transfer Learning in Medical Image Segmentation using Vision-Language ModelsarXiv preprint arXiv:2308.07706, 2024
Medical image segmentation with deep learning is an important and widely studied topic because segmentation enables quantifying target structure size and shape that can help in disease diagnosis, prognosis, surgery planning, and understanding. Recent advances in the foundation Vision-Language Models (VLMs) and their adaptation to segmentation tasks in natural images with Vision-Language Segmentation Models (VLSMs) have opened up a unique opportunity to build potentially powerful segmentation models for medical images that enable providing helpful information via language prompt as input, leverage the extensive range of other medical imaging datasets by pooled dataset training, adapt to new classes, and be robust against out-of-distribution data with human-in-the-loop prompting during inference. Although transfer learning from natural to medical images for imageonly segmentation models has been studied, no studies have analyzed how the joint representation of vision-language transfers to medical images in segmentation problems and understand gaps in leveraging their full potential. We present the first benchmark study on transfer learning of VLSMs to 2D medical images with thoughtfully collected 11 existing 2D medical image datasets of diverse modalities with carefully presented 9 types of language prompts from 14 attributes. Our results indicate that VLSMs trained in natural image-text pairs transfer reasonably to the medical domain in zero-shot settings when prompted appropriately for non-radiology photographic modalities; when finetuned, they obtain comparable performance to conventional architectures, even in X-rays and ultrasound modalities. However, the additional benefit of language prompts during finetuning may be limited, with image features playing a more dominant role; they can better handle training on pooled datasets combining diverse modalities and are potentially more robust to domain shift than the conventional segmentation models. The code and prompts are released at https://github.com/naamiinepal/medvlsm
@article{poudel2023exploring, title = {Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models}, author = {Poudel, Kanchan and Dhakal, Manish and Bhandari, Prasiddha and Adhikari, Rabin and Thapaliya, Safal and Khanal, Bishesh}, journal = {arXiv preprint arXiv:2308.07706}, year = {2024}, eprint = {2308.07706}, archiveprefix = {arXiv}, primaryclass = {cs.CV}, doi = {https://doi.org/10.48550/arXiv.2308.07706}, }
2023
2023
- MICCAISynthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in EchocardiographyIn International Workshop on Advances in Simplifying Medical Ultrasound , 2023
Accurate segmentation is essential for echocardiography-based assessment of cardiovascular diseases (CVDs). However, the variability among sonographers and the inherent challenges of ultrasound images hinder precise segmentation. By leveraging the joint representation of image and text modalities, Vision-Language Segmentation Models (VLSMs) can incorporate rich contextual information, potentially aiding in accurate and explainable segmentation. However, the lack of readily available data in echocardiography hampers the training of VLSMs. In this study, we explore using synthetic datasets from Semantic Diffusion Models (SDMs) to enhance VLSMs for echocardiography segmentation. We evaluate results for two popular VLSMs (CLIPSeg and CRIS) using seven different kinds of language prompts derived from several attributes, automatically extracted from echocardiography images, segmentation masks, and their metadata. Our results show improved metrics and faster convergence when pretraining VLSMs on SDM-generated synthetic images before finetuning on real images. The code and prompts are released at https://github.com/naamiinepal/synthetic-boost
@inproceedings{adhikari2023synthetic, title = {Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiography}, author = {Adhikari, Rabin and Dhakal, Manish and Thapaliya, Safal and Poudel, Kanchan and Bhandari, Prasiddha and Khanal, Bishesh}, booktitle = {International Workshop on Advances in Simplifying Medical Ultrasound}, pages = {89--99}, year = {2023}, organization = {Springer}, }
2022
2022
- IEEEAutomatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNetIn 2022 International Conference on Inventive Computation Technologies (ICICT) , 2022
This paper presents an end-to-end deep learning model for Automatic Speech Recognition (ASR) that transcribes Nepali speech to text. The model was trained and tested on the OpenSLR (audio, text) dataset. The majority of the audio dataset have silent gaps at both ends which are clipped during dataset preprocessing for a more uniform mapping of audio frames and their corresponding texts. Mel Frequency Cepstral Coe cients (MFCCs) are used as audio features to feed into the model. The model having Bidirectional LSTM paired with ResNet and one-dimensional CNN produces the best results for this dataset out of all the models (neural networks with variations of LSTM, GRU, CNN, and ResNet) that have been trained so far. This novel model uses Connectionist Temporal Classification (CTC) function for loss calculation during training and CTC beam search decoding for predicting characters as the most likely sequence of Nepali text. On the test dataset, the character error rate (CER) of 17.06 percent has been achieved. The source code is available at: https://github.com/manishdhakal/ASR-Nepali-using-CNN-BiLSTM-ResNet.
@inproceedings{dhakal2022automatic, title = {Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNet}, author = {Dhakal, Manish and Chhetri, Arman and Gupta, Aman Kumar and Lamichhane, Prabin and Pandey, Suraj and Shakya, Subarna}, booktitle = {2022 International Conference on Inventive Computation Technologies (ICICT)}, pages = {515--521}, year = {2022}, organization = {IEEE}, doi = {https://doi.org/10.1109/ICICT54344.2022.9850832}, }