Manish Dhakal

Khumaltar, Lalitpur,

Bagmati, Nepal

Computer Engineer & Machine Learning Researcher

Hello, I’m Manish Dhakal, a Computer Engineering graduate from the Institute of Engineering, Pulchowk Campus, Tribhuvan University. Currently, I am working as a Research Assistant (RA) at NAAMII, specializing in segmentation and object detection tasks within the field of medical imaging.

My research projects encompass both unimodal and multimodal (esp. vision-language ) learning, for the purpose of segmentation and object detection in medical imaging. I have also been involved in a project of the segmentation task of 3D mesh data. My passion lies in exploring the potential of multimodal learning techniques.

Prior to my current role, I gained valuable experience as an intern in blockchain engineering and also worked as a fullstack developer and co-founder at a Nepal-based startup, Techniti Nepal. These experiences broadened my skill set and provided insights into various areas of technology and entrepreneurship.

During my undergraduate studies, our academic team worked on capstone project of automatic speech recognition for the low-resourced Nepali Language which was later presented at an IEEE conference.

I’m currently looking for new ML/AI research opportunities. My desire to contribute to these sectors and investigate cutting-edge solutions is motivated by a sincere curiosity. I’m looking forward to pursuing relevant graduate programs to further my education and give back to the ML/AI community.

News

Apr 06, 2024	“Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models’’ has been accepted for MIDL 2024.
Nov 24, 2023	Attending ICVGIP 2023 conference on 15^th-17^th Dec, 2023, at IIT Ropar, India.
Aug 03, 2023	“Synthetic Boost’’ has been accepted for ASMUS 2023 (MICCAI Workshop).

Selected Publications

MIDL
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

Kanchan Poudel, Manish Dhakal, Prasiddha Bhandari, and 3 more authors

arXiv preprint arXiv:2308.07706, 2023

Abs arXiv Bib PDF Code

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 = {2023}, eprint = {2308.07706}, archiveprefix = {arXiv}, primaryclass = {cs.CV}, doi = {https://doi.org/10.48550/arXiv.2308.07706}, }